From 61e7501db39131b451eb3aa9d28dfd7e4f95f03a Mon Sep 17 00:00:00 2001 From: DouSam <28208639+DouSam@users.noreply.github.com> Date: Tue, 3 Mar 2026 05:15:14 +0000 Subject: [PATCH] Update translations from SlicerLanguageTranslations Processed tutorials: STC-GEN-101_WelcomeTutorial, STC-GEN-102_FourMinuteTutorial, STC-SEG-103_AIBasedSegmentationIn3DSlicer, STC-VIS-101_VisualizationTutorial Files: STC-GEN-101_en-US.ts,STC-GEN-101_es.ts,STC-GEN-101_fr.ts,STC-GEN-101_pt-BR.ts,STC-GEN-102_en-US.ts,STC-GEN-102_es.ts,STC-GEN-102_fr.ts,STC-GEN-102_pt-BR.ts,STC-SEG-103_ar-SA.ts,STC-SEG-103_en-US.ts,STC-SEG-103_es.ts,STC-SEG-103_fr.ts,STC-SEG-103_pt-BR.ts,STC-SEG-103_ta.ts,STC-SEG-103_th.ts,STC-SEG-103_uk.ts,STC-VIS-101_ar_SA.ts,STC-VIS-101_en-US.ts,STC-VIS-101_es.ts,STC-VIS-101_fr.ts,STC-VIS-101_pt-BR.ts --- .../Translations/text_dict_fr.json | 42 ++++---- .../Translations/text_dict_ar-SA.json | 36 +++---- .../Translations/text_dict_en-US.json | 36 +++---- .../Translations/text_dict_es.json | 55 +++-------- .../Translations/text_dict_fr.json | 55 +++-------- .../Translations/text_dict_pt-BR.json | 34 +++---- .../Translations/text_dict_ta.json | 51 +++------- .../Translations/text_dict_th.json | 37 ++++--- .../Translations/text_dict_uk.json | 55 +++-------- .../Translations/text_dict_ar_SA.json | 99 +++++++++++++++++++ 10 files changed, 246 insertions(+), 254 deletions(-) create mode 100644 Tutorials/STC-VIS-101_VisualizationTutorial/Translations/text_dict_ar_SA.json diff --git a/Tutorials/STC-GEN-101_WelcomeTutorial/Translations/text_dict_fr.json b/Tutorials/STC-GEN-101_WelcomeTutorial/Translations/text_dict_fr.json index 2e64122e..4666626b 100644 --- a/Tutorials/STC-GEN-101_WelcomeTutorial/Translations/text_dict_fr.json +++ b/Tutorials/STC-GEN-101_WelcomeTutorial/Translations/text_dict_fr.json @@ -4,43 +4,43 @@ "0_TextBox_1": "Sonia Pujol, Ph.D.", "1_TextBox_1": "Sonia Pujol, Ph.D.", "0_TextBox_2": "28/08/2024", - "0_TextBox_3": "Assistant Professor of Radiology\nBrigham and Women’s Hospital\nHarvard Medical School\n", + "0_TextBox_3": "Professeur adjoint en radiologie\nBrigham and Women’s Hospital\nHarvard Medical School\n", "2_Goal_title": "Objectif", "1_Goal_body": "Ce tutoriel est une brève introduction au module Welcome du logiciel open-source Slicer. ", "2_3DSlicerversion48_title": "3D Slicer version 4.8", "6_WelcometoSlicer_title": "Bienvenue dans Slicer", - "3_TextBox_0": "Slicer5 Basics", + "3_TextBox_0": "Notions de base Slicer5", "7_SlicerUserInterface_title": "Interface utilisateur de Slicer", - "4_TextBox_1": "3D Slicer 5 version 5.10.0 includes over 100 modules and more than 190 extensions for image segmentation, registration and 3D visualization of medical imaging data.", + "4_TextBox_1": "3D Slicer 5 version 5.10.0 comprend plus de 100 modules et plus de 190 extensions pour la segmentation d’images, le recalage et la visualisation 3D de données d’imagerie médicale.", "7_ArrowText_0": "Barre d’outils", "7_TextBox_1": "Visionneuse 3D", "7_ArrowText_6": "Visionneuses anatomiques 2D", "7_TextBox_5": "Sonde de données", - "1_TextBox_3": "Assistant Professor of Radiology\n\nBrigham and Women’s Hospital\n\nHarvard Medical School\n", - "2_Goal_body": "This tutorial is a short introduction to the Welcome module of the Slicer open-source software.", - "4_TextBox_0": "Slicer5 Basics", - "3_TextBox_1": "*Slicer is an open-source software for segmentation, registration and visualization of medical imaging data.\n*The platform is developed through a multi-institution effort of several NIH funded large-scale consortia.\n*Slicer is for medical research only, and is not FDA approved. ", + "1_TextBox_3": "Professeur adjoint en radiologie\n\nBrigham and Women’s Hospital\n\nHarvard Medical School\n", + "2_Goal_body": "Ce tutoriel est une courte introduction au module Welcome du logiciel open-source Slicer.", + "4_TextBox_0": "Notions de base Slicer5", + "3_TextBox_1": "*Slicer est un logiciel open-source dédié à la segmentation, au recalage et à la visualisation de données d’imagerie médicale.\n*La plateforme est développée grâce à un effort multi-institutionnel impliquant plusieurs grands consortiums financés par le NIH.\n*Slicer est destiné uniquement à la recherche médicale et n’est pas approuvé par la FDA. ", "5_TextBox_0": "L’onglet Documentation & Tutoriels \ncontient des liens vers \nle compendium de formation et les pages de documentation de la version 4.8 de 3D Slicer.\t", - "5_TextBox_1": "*Slicer is a multi-platform software developed and maintained on Mac OSX, Linux and Windows.\n\n*Slicer requires a minimum of 2 GB of RAM and a dedicated graphic accelerator with 64 MB of on-board graphic memory. ", - "6_TextBox_1": "Each module of Slicer includes a series of tabs, which give access to different functionalities.\n\nClick on the arrow symbol to display the content of each tab. ", - "7_TextBox_3": "User Interface (UI) panel of the Slicer Welcome Module", + "5_TextBox_1": "*Slicer est un logiciel multiplateforme développé et maintenu sur Mac OS X, Linux et Windows.\n\n*Slicer nécessite au minimum 2 Go de mémoire vive (RAM) ainsi qu’un accélérateur graphique dédié disposant de 64 Mo de mémoire vidéo embarquée.* ", + "6_TextBox_1": "Chaque module de Slicer comprend une série d’onglets qui donnent accès à différentes fonctionnalités.\n\nCliquez sur le symbole en forme de flèche pour afficher le contenu de chaque onglet. ", + "7_TextBox_3": "Panneau d’interface utilisateur (UI) du module Welcome de Slicer", "8_WelcomeModule_title": "Module Welcome", "9_WelcomeModule_title": "Module Welcome", "13_WelcomeModule_title": "Module Welcome", - "8_TextBox_1": "The Documentation & Tutorials tab contains links to the training compendium and documentation pages of 3D Slicer.", + "8_TextBox_1": "L’onglet Documentation & Tutorials contient des liens vers le compendium de formation et les pages de documentation de 3D Slicer.", "9_TextBox_0": "Le scan IRM du cerveau apparaît dans les visionneuses 2D.", - "10_SampleData_title": "Sample Data", - "11_SampleData_title": "Sample Data", - "12_SampleData_title": "Sample Data", - "10_TextBox_1": "The Sample Data module contains links to different sample datasets that can be downloaded into Slicer.", + "10_SampleData_title": "Données exemples", + "11_SampleData_title": "Données exemples", + "12_SampleData_title": "Données exemples", + "10_TextBox_1": "Le module Sample Data contient des liens vers différents jeux de données exemples pouvant être téléchargés dans Slicer.", "11_TextBox_0": "Cliquez sur l’icône de lien pour relier les trois visionneuses 2D, puis sur l’icône en forme d’œil à côté pour afficher les coupes dans la visionneuse 3D\t) ;", - "11_TextBox_1": "Chest CT", - "11_TextBox_2": "Cardiac CT", - "11_TextBox_3": "Diffusion Tensor Imaging (DTI) Dataset", - "11_TextBox_4": "Brain MRI (tumor patient)", - "12_ArrowText_0": "Click on MRHead to download the\ndataset in Slicer.", + "11_TextBox_1": "Scanner thoracique", + "11_TextBox_2": "Scanner cardiaque", + "11_TextBox_3": "Jeu de données d’imagerie par tenseur de diffusion (DTI)", + "11_TextBox_4": "IRM cérébrale (patient atteint d’une tumeur)", + "12_ArrowText_0": "Cliquez sur MRHead pour télécharger le jeu de données dans Slicer.", "13_TextBox_0": "Pour en savoir plus sur Slicer et ses différentes fonctionnalités, consultez le compendium de Slicer 4.8.", - "14_TextBox_0": "Position the mouse on the little pin icon in the top left corner of the red viewer to display the viewer menu", + "14_TextBox_0": "Placez la souris sur la petite icône en forme d’épingle dans le coin supérieur gauche du visualiseur rouge pour afficher le menu du visualiseur", "15_TextBox_1": "Click on the link icon to link all three 2D viewers, and on the eye icon next to it\nto display the slices in the 3D viewer", "16_TextBox_0": "The axial, coronal and sagittal slices appear in the 3D viewer.\nGo back to the Welcome module using the green arrow in the toolbar", "17_GoingFurther_title": "Going Further", diff --git a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_ar-SA.json b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_ar-SA.json index 119c0bd9..f7d01c66 100644 --- a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_ar-SA.json +++ b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_ar-SA.json @@ -1,49 +1,49 @@ { "0_TextBox_0": "AI-based Segmentation in 3D Slicer", - "0_TextBox_1": "Sonia Pujol, Ph. D\nBrigham and Women's Hospital,\nHarvard Medical School\nBoston, MA", + "0_TextBox_1": "Sonia Pujol, Ph. D. \nBrigham and Women's Hospital,\nHarvard Medical School\nBoston, MA", "0_TextBox_3": "Slicer Ribeirão Preto Workshop\nJune 30, 2025", "1_TextBox_0": "Manual vs AI-powered Segmentation", "2_TextBox_0": "Manual vs AI-powered Segmentation", "1_TextBox_1": "Medical images have traditionally been manually segmented, which is a time-consuming process that requires intensive effort by radiologists and is subject to inter-reader variability.", "2_TextBox_1": "In the past decade, image segmentation has been powered by the development of deep learning algorithms (e.g. nnUnet by the German Cancer Research Center (DKFZ)/Helmholtz Research).\n\n\nAI-powered segmentation tools can reduce the segmentation time and provide more reproducible results.", "3_TextBox_0": "AI Terminology", - "3_TextBox_1": "A is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a Model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", - "4_TextBox_0": "3D Slicer AI Workshop", - "4_TextBox_1": "This tutorial focuses on running inference tasks using various pretrained AI models for automated segmentation of anatomical and pathological structures.", + "3_TextBox_1": "A Model is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", + "4_TextBox_0": "3D Slicer AI Tutorial", + "4_TextBox_1": "This tutorial focuses on running inference tasks using various pre-trained AI models for automated segmentation of anatomical and pathological structures.", "5_TextBox_0": "MONAIAuto3DSeg Slicer extension", "6_TextBox_0": "MONAIAuto3DSeg Slicer extension", "5_TextBox_1": "This tutorial uses the pre-trained models of the MONAIAuto3DSeg Slicer extension.\n\n\nThe tool is designed to work on laptops or on average desktop computer without a GPU.", "6_TextBox_1": "Multiple modalities Support (CT, MRI).\n\n\nMultiple anatomies (head, thorax, abdomen, pelvis, etc.).\n\n\nMultiple pathologies (tumor, hemorrhage, edema).", "7_TextBox_0": "Slicer AI Tutorial: Segmentation Tasks", - "7_TextBox_1": "Segmentation Task #1: Prostate.\n\n\nSegmentation Task #2: Brain Glioma.\n\n\nSegmentation Task #3: Whole Body Segmentation.", + "7_TextBox_1": "Segmentation Task #1: Prostate \n\n\nSegmentation Task #2: Brain Glioma \n\n\nSegmentation Task #3: Whole Body Segmentation", "8_TextBox_0": "AI Segmentation Task #1: Prostate", "9_TextBox_1": "AI-based Segmentation of Peripheral Zone (PZ) and Transition Zone (TZ) of the prostate on T2-weighted MRI Images.\n\n\nDataset:\nmsd_prostate_01-t2\nmsd_prostate_01-adc", - "10_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose Directory to Add and browse to the location of the Slicer datasets", - "11_TextBox_0": "Slicer loads the prostate MRI data set", - "12_TextBox_0": "Click on Welcome to Slicer in the Modules' menu and browse to the category Segmentation\n\nSelec the MONAI Auto3DSeg module", + "10_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose Directory to Add and browse to the location of the Slicer datasets\n\nSelect the dataset3_ProstateMRI and click on Open", + "11_TextBox_0": "Slicer loads the prostate MRI dataset", + "12_TextBox_0": "Click on Welcome to Slicer in the Modules' menu and browse to the category Segmentation\n\nSelect the MONAIAuto3DSeg module", "13_TextBox_0": "Enter the model's name Prostate in the Segmentation model menu", "13_TextBox_1": "Select the model Prostate - Multisequence", - "14_TextBox_0": "Enter the Input T2 volume msd-prostate-01-adc and the Input ADC volume msd-prostate-01-adc.", + "14_TextBox_0": "Enter the Input T2 volume msd-prostate-01-t2 and the Input ADC volume msd-prostate-01-adc", "14_TextBox_1": "Click on Create new segmentation on Apply", + "15_TextBox_0": "Slicer starts the inference", "16_TextBox_0": "Slicer shows the results of the AI-based prostate segmentation", "17_TextBox_0": "AI Segmentation Task #2: Brain Glioma", "18_TextBox_1": "AI-based Segmentation of Neoplasm, Necrosis and Edema in Brain MRI images.\n\n\nDatasets:\n1) BraTS-GLI_00005-000-t1n (T1-weighted)\n2) BraTS-GLI_00005-000-t1c (T1-weighted post-Gd)\n3) BraTS-GLI_00005-000-t2w (T2-weighted)\n4) BraTS-GLI_00005-000-t2f (T2-FLAIR )", - "19_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose File(s) to Add and browse to the loccation of the Slicer datasets", + "19_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose File(s) to Add and browse to the location of the Slicer datasets\n\nIn the subdirectory dataset4_BrainMRI_Glioma, select the four datasets BraTS-GLI-00006-t1c.nii.gz, BraTS-GLI-00006-t1n.nii.gz, BraTS-GLI-00006-t2f.nii.gz, BraTS-GLI-00006-t2w.nii.gz\n\nClick on Open", "20_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Brain Tumor Segmentation in the Segmentation model menu", "20_TextBox_1": "Select the model Brain Tumor Segmentation (BRATS) GLI", - "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t12f\nInput T1C volume: BraTS-GLI_00005-00-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Appply\n\nClick on Apply to start the segmentation", + "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t2f\nInput T1C volume: BraTS-GLI_00005-000-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", "22_TextBox_1": "Slicer starts running the inference task\n\nOnce the segmentation is done, 'Processing finished' appears in the Slicer GUI", "23_TextBox_1": "Click on Show 3D to display the 3D segments in the 3D Viewer", "24_TextBox_0": "AI Segmentation Task #3: Whole Body Segmentation", "25_TextBox_1": "AI-based Segmentation of the whole body.\n\n\nDataset:\nCT_ThoraxAbdomen", "26_TextBox_0": "In the Add DICOM Data module, select the Patient patient1 and double click onthe image CT_Thorax_Abdomen to load it in Slicer", - "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", + "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", "27_TextBox_1": "Select the model Whole Body Segmentation TS1-quick", - "28_TextBox_0": "Select the input Volume 6: CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", - "29_TextBox_0": "Slicer displays the results of the IA-based segmentation using the Whole Body Segmentation TS1-quick", + "28_TextBox_0": "Select the input Volume 6:CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", + "29_TextBox_0": "Slicer displays the results of the AI-based segmentation using the Whole Body Segmentation TS1-quick", "30_TextBox_0": "Conclusion", - "30_TextBox_1": "The 3D SlicerMONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", - "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible by two CZI Essential Open Source Software for Science (EOSS cycle 4 & 5) grants.", - "15_TextBox_0": "Slicer starts the inference", - "31_TextBox_0": "Acknowledgements" + "30_TextBox_1": "The 3D Slicer MONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", + "31_TextBox_0": "Acknowledgements", + "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible through funding by the Chan Zuckerberg Initiative." } \ No newline at end of file diff --git a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_en-US.json b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_en-US.json index 119c0bd9..f7d01c66 100644 --- a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_en-US.json +++ b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_en-US.json @@ -1,49 +1,49 @@ { "0_TextBox_0": "AI-based Segmentation in 3D Slicer", - "0_TextBox_1": "Sonia Pujol, Ph. D\nBrigham and Women's Hospital,\nHarvard Medical School\nBoston, MA", + "0_TextBox_1": "Sonia Pujol, Ph. D. \nBrigham and Women's Hospital,\nHarvard Medical School\nBoston, MA", "0_TextBox_3": "Slicer Ribeirão Preto Workshop\nJune 30, 2025", "1_TextBox_0": "Manual vs AI-powered Segmentation", "2_TextBox_0": "Manual vs AI-powered Segmentation", "1_TextBox_1": "Medical images have traditionally been manually segmented, which is a time-consuming process that requires intensive effort by radiologists and is subject to inter-reader variability.", "2_TextBox_1": "In the past decade, image segmentation has been powered by the development of deep learning algorithms (e.g. nnUnet by the German Cancer Research Center (DKFZ)/Helmholtz Research).\n\n\nAI-powered segmentation tools can reduce the segmentation time and provide more reproducible results.", "3_TextBox_0": "AI Terminology", - "3_TextBox_1": "A is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a Model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", - "4_TextBox_0": "3D Slicer AI Workshop", - "4_TextBox_1": "This tutorial focuses on running inference tasks using various pretrained AI models for automated segmentation of anatomical and pathological structures.", + "3_TextBox_1": "A Model is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", + "4_TextBox_0": "3D Slicer AI Tutorial", + "4_TextBox_1": "This tutorial focuses on running inference tasks using various pre-trained AI models for automated segmentation of anatomical and pathological structures.", "5_TextBox_0": "MONAIAuto3DSeg Slicer extension", "6_TextBox_0": "MONAIAuto3DSeg Slicer extension", "5_TextBox_1": "This tutorial uses the pre-trained models of the MONAIAuto3DSeg Slicer extension.\n\n\nThe tool is designed to work on laptops or on average desktop computer without a GPU.", "6_TextBox_1": "Multiple modalities Support (CT, MRI).\n\n\nMultiple anatomies (head, thorax, abdomen, pelvis, etc.).\n\n\nMultiple pathologies (tumor, hemorrhage, edema).", "7_TextBox_0": "Slicer AI Tutorial: Segmentation Tasks", - "7_TextBox_1": "Segmentation Task #1: Prostate.\n\n\nSegmentation Task #2: Brain Glioma.\n\n\nSegmentation Task #3: Whole Body Segmentation.", + "7_TextBox_1": "Segmentation Task #1: Prostate \n\n\nSegmentation Task #2: Brain Glioma \n\n\nSegmentation Task #3: Whole Body Segmentation", "8_TextBox_0": "AI Segmentation Task #1: Prostate", "9_TextBox_1": "AI-based Segmentation of Peripheral Zone (PZ) and Transition Zone (TZ) of the prostate on T2-weighted MRI Images.\n\n\nDataset:\nmsd_prostate_01-t2\nmsd_prostate_01-adc", - "10_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose Directory to Add and browse to the location of the Slicer datasets", - "11_TextBox_0": "Slicer loads the prostate MRI data set", - "12_TextBox_0": "Click on Welcome to Slicer in the Modules' menu and browse to the category Segmentation\n\nSelec the MONAI Auto3DSeg module", + "10_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose Directory to Add and browse to the location of the Slicer datasets\n\nSelect the dataset3_ProstateMRI and click on Open", + "11_TextBox_0": "Slicer loads the prostate MRI dataset", + "12_TextBox_0": "Click on Welcome to Slicer in the Modules' menu and browse to the category Segmentation\n\nSelect the MONAIAuto3DSeg module", "13_TextBox_0": "Enter the model's name Prostate in the Segmentation model menu", "13_TextBox_1": "Select the model Prostate - Multisequence", - "14_TextBox_0": "Enter the Input T2 volume msd-prostate-01-adc and the Input ADC volume msd-prostate-01-adc.", + "14_TextBox_0": "Enter the Input T2 volume msd-prostate-01-t2 and the Input ADC volume msd-prostate-01-adc", "14_TextBox_1": "Click on Create new segmentation on Apply", + "15_TextBox_0": "Slicer starts the inference", "16_TextBox_0": "Slicer shows the results of the AI-based prostate segmentation", "17_TextBox_0": "AI Segmentation Task #2: Brain Glioma", "18_TextBox_1": "AI-based Segmentation of Neoplasm, Necrosis and Edema in Brain MRI images.\n\n\nDatasets:\n1) BraTS-GLI_00005-000-t1n (T1-weighted)\n2) BraTS-GLI_00005-000-t1c (T1-weighted post-Gd)\n3) BraTS-GLI_00005-000-t2w (T2-weighted)\n4) BraTS-GLI_00005-000-t2f (T2-FLAIR )", - "19_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose File(s) to Add and browse to the loccation of the Slicer datasets", + "19_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose File(s) to Add and browse to the location of the Slicer datasets\n\nIn the subdirectory dataset4_BrainMRI_Glioma, select the four datasets BraTS-GLI-00006-t1c.nii.gz, BraTS-GLI-00006-t1n.nii.gz, BraTS-GLI-00006-t2f.nii.gz, BraTS-GLI-00006-t2w.nii.gz\n\nClick on Open", "20_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Brain Tumor Segmentation in the Segmentation model menu", "20_TextBox_1": "Select the model Brain Tumor Segmentation (BRATS) GLI", - "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t12f\nInput T1C volume: BraTS-GLI_00005-00-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Appply\n\nClick on Apply to start the segmentation", + "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t2f\nInput T1C volume: BraTS-GLI_00005-000-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", "22_TextBox_1": "Slicer starts running the inference task\n\nOnce the segmentation is done, 'Processing finished' appears in the Slicer GUI", "23_TextBox_1": "Click on Show 3D to display the 3D segments in the 3D Viewer", "24_TextBox_0": "AI Segmentation Task #3: Whole Body Segmentation", "25_TextBox_1": "AI-based Segmentation of the whole body.\n\n\nDataset:\nCT_ThoraxAbdomen", "26_TextBox_0": "In the Add DICOM Data module, select the Patient patient1 and double click onthe image CT_Thorax_Abdomen to load it in Slicer", - "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", + "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", "27_TextBox_1": "Select the model Whole Body Segmentation TS1-quick", - "28_TextBox_0": "Select the input Volume 6: CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", - "29_TextBox_0": "Slicer displays the results of the IA-based segmentation using the Whole Body Segmentation TS1-quick", + "28_TextBox_0": "Select the input Volume 6:CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", + "29_TextBox_0": "Slicer displays the results of the AI-based segmentation using the Whole Body Segmentation TS1-quick", "30_TextBox_0": "Conclusion", - "30_TextBox_1": "The 3D SlicerMONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", - "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible by two CZI Essential Open Source Software for Science (EOSS cycle 4 & 5) grants.", - "15_TextBox_0": "Slicer starts the inference", - "31_TextBox_0": "Acknowledgements" + "30_TextBox_1": "The 3D Slicer MONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", + "31_TextBox_0": "Acknowledgements", + "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible through funding by the Chan Zuckerberg Initiative." } \ No newline at end of file diff --git a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_es.json b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_es.json index ae633758..3aa2aa14 100644 --- a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_es.json +++ b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_es.json @@ -1,26 +1,21 @@ { - "1_0_ArrowText_0": "Pulse sobre Añadir Datos en el Bienvenido\nen el módulo Slicer", - "2_2_TextBox_1": "Pulse en Elegir Directorio para añadir y\nexplorar el lugar de las bases de datos\nSlicer", - "3_0_TextBox_0": "Slicer carga el conjunto de datos de\nresonancia magnética de próstata", - "4_3_TextBox_1": "Pulse sobre Bienvenido a Rebanada en\nel menú de Módulos y examinar a la\n\ncategoría Segmentación.\n\nSeleccione el módulo\n MONAI Auto3DSeg", - "5_0_ArrowText_0": "Introduzca el nombre Próstata del modelo\nen el menú de modelo Segmentación", "0_TextBox_0": "AIBasedSegmentationIn3DSlicer", "0_TextBox_1": "Sonia Pujol, Ph.D.", "0_TextBox_3": "Segmentación basada en IA en 3D Slicer", "1_TextBox_0": "Manual vs AI-powered Segmentation", - "2_TextBox_0": "Slicer carga el conjunto de datos de resonancia magnética de próstata", + "2_TextBox_0": "Manual vs AI-powered Segmentation", "1_TextBox_1": "Clic en Agregar datos en el módulo Bienvenido a \tSlicer", "2_TextBox_1": "In the past decade, image segmentation has been powered by the development of deep learning algorithms (e.g. nnUnet by the German Cancer Research Center (DKFZ)/Helmholtz Research).\n\n\nAI-powered segmentation tools can reduce the segmentation time and provide more reproducible results.", "3_TextBox_0": "AI Terminology", - "3_TextBox_1": "A is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a Model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", - "4_TextBox_0": "3D Slicer AI Workshop", - "4_TextBox_1": "This tutorial focuses on running inference tasks using various pretrained AI models for automated segmentation of anatomical and pathological structures.", + "3_TextBox_1": "A Model is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", + "4_TextBox_0": "3D Slicer AI Tutorial", + "4_TextBox_1": "This tutorial focuses on running inference tasks using various pre-trained AI models for automated segmentation of anatomical and pathological structures.", "5_TextBox_0": "MONAIAuto3DSeg Slicer extension", "6_TextBox_0": "MONAIAuto3DSeg Slicer extension", "5_TextBox_1": "This tutorial uses the pre-trained models of the MONAIAuto3DSeg Slicer extension.\n\n\nThe tool is designed to work on laptops or on average desktop computer without a GPU.", "6_TextBox_1": "Clic en Crear nueva segmentación en\nAplicar", "7_TextBox_0": "Slicer AI Tutorial: Segmentation Tasks", - "7_TextBox_1": "Segmentation Task #1: Prostate.\n\n\nSegmentation Task #2: Brain Glioma.\n\n\nSegmentation Task #3: Whole Body Segmentation.", + "7_TextBox_1": "Segmentation Task #1: Prostate \n\n\nSegmentation Task #2: Brain Glioma \n\n\nSegmentation Task #3: Whole Body Segmentation", "8_TextBox_0": "Slicer muestra el resultado de la segmentación prostática basada en AI", "9_TextBox_1": "AI-based Segmentation of Peripheral Zone (PZ) and Transition Zone (TZ) of the prostate on T2-weighted MRI Images.\n\n\nDataset:\nmsd_prostate_01-t2\nmsd_prostate_01-adc", "10_TextBox_1": "Selecciona el modelo Segmentación de Tumor Cerebral\n(BRATS) GLI", @@ -30,49 +25,25 @@ "13_TextBox_1": "Seleccione el modelo Próstata – Multisecuencia", "14_TextBox_0": "En el añadir módulo de datos DICOM\nseleccione al Paciente paciente1 y\ndoble clic en la imagen\nCT_Torax_Abdomen para cargarlo en\nSlicer", "14_TextBox_1": "Click on Create new segmentation on Apply", + "15_TextBox_0": "Slicer inicia la inferencia", "16_TextBox_0": "Seleccionar el input Volumen 6: CT_Torax_Abdomen\nClic en Crear nueva Segmentación en Aplicar\nClic en Aplicar para iniciar la segmentación", "17_TextBox_0": "AI Segmentation Task #2: Brain Glioma", "18_TextBox_1": "AI-based Segmentation of Neoplasm, Necrosis and Edema in Brain MRI images.\n\n\nDatasets:\n1) BraTS-GLI_00005-000-t1n (T1-weighted)\n2) BraTS-GLI_00005-000-t1c (T1-weighted post-Gd)\n3) BraTS-GLI_00005-000-t2w (T2-weighted)\n4) BraTS-GLI_00005-000-t2f (T2-FLAIR )", - "19_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose File(s) to Add and browse to the loccation of the Slicer datasets", + "19_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose File(s) to Add and browse to the location of the Slicer datasets\n\nIn the subdirectory dataset4_BrainMRI_Glioma, select the four datasets BraTS-GLI-00006-t1c.nii.gz, BraTS-GLI-00006-t1n.nii.gz, BraTS-GLI-00006-t2f.nii.gz, BraTS-GLI-00006-t2w.nii.gz\n\nClick on Open", "20_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Brain Tumor Segmentation in the Segmentation model menu", "20_TextBox_1": "Select the model Brain Tumor Segmentation (BRATS) GLI", - "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t12f\nInput T1C volume: BraTS-GLI_00005-00-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Appply\n\nClick on Apply to start the segmentation", + "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t2f\nInput T1C volume: BraTS-GLI_00005-000-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", "22_TextBox_1": "Slicer starts running the inference task\n\nOnce the segmentation is done, 'Processing finished' appears in the Slicer GUI", "23_TextBox_1": "Click on Show 3D to display the 3D segments in the 3D Viewer", "24_TextBox_0": "AI Segmentation Task #3: Whole Body Segmentation", "25_TextBox_1": "AI-based Segmentation of the whole body.\n\n\nDataset:\nCT_ThoraxAbdomen", "26_TextBox_0": "In the Add DICOM Data module, select the Patient patient1 and double click onthe image CT_Thorax_Abdomen to load it in Slicer", - "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", + "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", "27_TextBox_1": "Select the model Whole Body Segmentation TS1-quick", - "28_TextBox_0": "Select the input Volume 6: CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", - "29_TextBox_0": "Slicer displays the results of the IA-based segmentation using the Whole Body Segmentation TS1-quick", + "28_TextBox_0": "Select the input Volume 6:CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", + "29_TextBox_0": "Slicer displays the results of the AI-based segmentation using the Whole Body Segmentation TS1-quick", "30_TextBox_0": "Conclusion", - "30_TextBox_1": "The 3D SlicerMONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", - "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible by two CZI Essential Open Source Software for Science (EOSS cycle 4 & 5) grants.", - "7_0_TextBox_0": "Introduzca el volumen T2 de entrada\nmsd-prostate-01-adc e introduzca el\nvolumen ADC msd-prostate-01-adc", - "7_0_ArrowText_1": "Seleccione Crear segmentación nueva\nen Aplicar", - "7_0_ArrowText_2": "Pulse sobre Aplicar", - "8_0_TextBox_0": "El rebanador inicia la inferencia", - "9_0_TextBox_0": "El rebanador muestra el resultado del\nIA-basada en segmentación de próstata", - "10_2_TextBox_2": "Pulse en Añadir Datos en el módulo\nBienvenido al módulo Rebanador\n\n\n\n\n\n\n\n\n\n\nPulse sobre Elegir Archivo(s) para Añadir y\nexaminar al lugar del conjunto de datos de\nla base de datos Rebanador", - "0_TextBox_2": "30/06/2025", - "3_ArrowText_0": "Clic en Bienvenido a Slicer en el\nMenú de módulos y navegar a\n la categoría Segmentación.", - "4_ArrowText_0": "Ingrese el nombre del modelo Próstata en\nel menú del modelo de segmentación", - "5_ArrowText_0": "Ingresar el volumen de entrada T2 msdprostate-01-adc", - "6_ArrowText_0": "Y el volumen ADC de entrada msd-prostate-01-adc", - "15_TextBox_0": "Slicer inicia la inferencia", - "9_TextBox_0": "Cargar dataset4_BrainMRI_Glioma", - "10_ArrowText_0": "Seleccione el módulo MONAIAuto3DSeg e introduzca\nel nombre de modelo Segmentación de Tumor Cerebral\nen el menú del modelo Segmentación", - "13_ArrowText_1": "Una vez que la segmentación esté hecha, 'Procesamiento terminado'\naparece en el IGU Slicer", - "15_ArrowText_0": "Seleccionar el módulo MONAIAAuto3DSeg y entrar el\nnombre del modelo Segmentación de Cuerpo Completo en el\nmenú de modelo de Segmentación);", - "15_ArrowText_1": "Seleccionar el módulo MONAIAAuto3DSeg y entrar el\nnombre del modelo Segmentación de Cuerpo Completo en el\nmenú de modelo de Segmentación);", - "18_TextBox_0": "Slicer exhibe los resultados de la segmentación basada en IA\nusando la Segmentación de Cuerpo Completo TS1- rápida", + "30_TextBox_1": "The 3D Slicer MONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", "31_TextBox_0": "Agradecimientos", - "13_0_TextBox_0": "Introduzca los volúmenes de entrada como sigue:", - "16_2_TextBox_0": "En el módulo Añadir Datos DICOM,\nselecciona el Paciente paciente1 y\npulsa en Cargar para cargarlo en el\nRebanador", - "17_0_TextBox_0": "Seleccione el módulo MONAIAuto3DSeg e introduzca\nel nombre del modelo Segmentación de Cuerpo Completo\n\nen el menú Segmentación del modelo", - "17_0_ArrowText_1": "Seleccione el modelo Segmentación de Cuerpo Completo\nTS1 - rápido", - "18_0_TextBox_3": "Seleccione la entrada del Volumen 6: TC_Tórax_Abdomen.\n\nPulse en Crear segmentación nueva sobre Aplicar\nPulse en Aplicar para iniciar la segmentación", - "19_0_TextBox_0": "Rebanador comienza la segmentación", - "20_0_TextBox_1": "Pulse sobre Mostrar 2D para\n\nver el segmento en vista 3D" + "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible through funding by the Chan Zuckerberg Initiative." } \ No newline at end of file diff --git a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_fr.json b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_fr.json index c4a8dbfa..42a301e5 100644 --- a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_fr.json +++ b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_fr.json @@ -1,26 +1,21 @@ { - "1_0_ArrowText_0": "Cliquez sur Ajouter des données dans le module Bienvenue \ndans Slicer", - "2_2_TextBox_1": "Cliquez sur Choisir un répertoire pour ajouter\net naviguez jusqu'à l'emplacement des\njeux de données Slicer", - "3_0_TextBox_0": "Slicer charge l'IRM de la prostate\nbase de données", - "4_3_TextBox_1": "Cliquez sur Bienvenue dans Slicer dans le\nmenu Modules et naviguez jusqu'à\n\nla catégorie Segmentation.\n\nSélectionnez le module MONAI Auto3DSeg", - "5_0_ArrowText_0": "Saisissez le nom du modèle Prostate dans\n le menu Modèle de segmentation", "0_TextBox_0": "AIBasedSegmentationIn3DSlicer", "0_TextBox_1": "Sonia Pujol, Ph.D.", "0_TextBox_3": "Segmentation basée sur l'IA dans 3D Slicer", "1_TextBox_0": "Manual vs AI-powered Segmentation", - "2_TextBox_0": "Slicer charge l'ensemble de données IRM de la prostate", + "2_TextBox_0": "Manual vs AI-powered Segmentation", "1_TextBox_1": "Cliquez sur Add Data dans le module Welcome to Slicer", "2_TextBox_1": "In the past decade, image segmentation has been powered by the development of deep learning algorithms (e.g. nnUnet by the German Cancer Research Center (DKFZ)/Helmholtz Research).\n\n\nAI-powered segmentation tools can reduce the segmentation time and provide more reproducible results.", "3_TextBox_0": "AI Terminology", - "3_TextBox_1": "A is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a Model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", - "4_TextBox_0": "3D Slicer AI Workshop", - "4_TextBox_1": "This tutorial focuses on running inference tasks using various pretrained AI models for automated segmentation of anatomical and pathological structures.", + "3_TextBox_1": "A Model is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", + "4_TextBox_0": "3D Slicer AI Tutorial", + "4_TextBox_1": "This tutorial focuses on running inference tasks using various pre-trained AI models for automated segmentation of anatomical and pathological structures.", "5_TextBox_0": "MONAIAuto3DSeg Slicer extension", "6_TextBox_0": "MONAIAuto3DSeg Slicer extension", "5_TextBox_1": "This tutorial uses the pre-trained models of the MONAIAuto3DSeg Slicer extension.\n\n\nThe tool is designed to work on laptops or on average desktop computer without a GPU.", "6_TextBox_1": "Cliquez sur Create new segmentation sur Apply", "7_TextBox_0": "Slicer AI Tutorial: Segmentation Tasks", - "7_TextBox_1": "Segmentation Task #1: Prostate.\n\n\nSegmentation Task #2: Brain Glioma.\n\n\nSegmentation Task #3: Whole Body Segmentation.", + "7_TextBox_1": "Segmentation Task #1: Prostate \n\n\nSegmentation Task #2: Brain Glioma \n\n\nSegmentation Task #3: Whole Body Segmentation", "8_TextBox_0": "Slicer affiche le résultat de la segmentation de la prostate basée sur l’IA", "9_TextBox_1": "AI-based Segmentation of Peripheral Zone (PZ) and Transition Zone (TZ) of the prostate on T2-weighted MRI Images.\n\n\nDataset:\nmsd_prostate_01-t2\nmsd_prostate_01-adc", "10_TextBox_1": "Sélectionner le modèle Brain Tumor Segmentation\n(BRATS) GLI", @@ -30,49 +25,25 @@ "13_TextBox_1": "Sélectionner le modèle Prostate - Multiséquence", "14_TextBox_0": "Dans le module Add DICOM Data, sélectionnez le patient patient1 et double-cliquez sur l’image CT_Thorax_Abdomen pour la charger dans Slicer", "14_TextBox_1": "Click on Create new segmentation on Apply", + "15_TextBox_0": "Slicer démarre l’inférence", "16_TextBox_0": "Sélectionnez le volume d’entrée 6 : CT_Thorax_Abdomen\nCliquez sur Create new Segmentation dans Apply \nCliquez sur Apply pour démarrer la segmentation", "17_TextBox_0": "AI Segmentation Task #2: Brain Glioma", "18_TextBox_1": "AI-based Segmentation of Neoplasm, Necrosis and Edema in Brain MRI images.\n\n\nDatasets:\n1) BraTS-GLI_00005-000-t1n (T1-weighted)\n2) BraTS-GLI_00005-000-t1c (T1-weighted post-Gd)\n3) BraTS-GLI_00005-000-t2w (T2-weighted)\n4) BraTS-GLI_00005-000-t2f (T2-FLAIR )", - "19_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose File(s) to Add and browse to the loccation of the Slicer datasets", + "19_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose File(s) to Add and browse to the location of the Slicer datasets\n\nIn the subdirectory dataset4_BrainMRI_Glioma, select the four datasets BraTS-GLI-00006-t1c.nii.gz, BraTS-GLI-00006-t1n.nii.gz, BraTS-GLI-00006-t2f.nii.gz, BraTS-GLI-00006-t2w.nii.gz\n\nClick on Open", "20_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Brain Tumor Segmentation in the Segmentation model menu", "20_TextBox_1": "Select the model Brain Tumor Segmentation (BRATS) GLI", - "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t12f\nInput T1C volume: BraTS-GLI_00005-00-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Appply\n\nClick on Apply to start the segmentation", + "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t2f\nInput T1C volume: BraTS-GLI_00005-000-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", "22_TextBox_1": "Slicer starts running the inference task\n\nOnce the segmentation is done, 'Processing finished' appears in the Slicer GUI", "23_TextBox_1": "Click on Show 3D to display the 3D segments in the 3D Viewer", "24_TextBox_0": "AI Segmentation Task #3: Whole Body Segmentation", "25_TextBox_1": "AI-based Segmentation of the whole body.\n\n\nDataset:\nCT_ThoraxAbdomen", "26_TextBox_0": "In the Add DICOM Data module, select the Patient patient1 and double click onthe image CT_Thorax_Abdomen to load it in Slicer", - "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", + "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", "27_TextBox_1": "Select the model Whole Body Segmentation TS1-quick", - "28_TextBox_0": "Select the input Volume 6: CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", - "29_TextBox_0": "Slicer displays the results of the IA-based segmentation using the Whole Body Segmentation TS1-quick", + "28_TextBox_0": "Select the input Volume 6:CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", + "29_TextBox_0": "Slicer displays the results of the AI-based segmentation using the Whole Body Segmentation TS1-quick", "30_TextBox_0": "Conclusion", - "30_TextBox_1": "The 3D SlicerMONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", - "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible by two CZI Essential Open Source Software for Science (EOSS cycle 4 & 5) grants.", - "7_0_TextBox_0": "Entrez le volume T2 d'entrée msd-\nprostate-01-adc et le volume ADC \nd'entrée msd-prostate-01-adc", - "7_0_ArrowText_1": "Sélectionner Créer une nouvelle segmentation sur\nAppliquer", - "7_0_ArrowText_2": "Cliquez sur Appliquer", - "8_0_TextBox_0": "Slicer commence l'inférence", - "9_0_TextBox_0": "Slicer montre le résultat de la segmentation \nde la prostate basée sur l'IA", - "10_2_TextBox_2": "Cliquez sur Ajouter des données dans le module Bienvenue\nà Slicer\n\n\n\n\n\n\n\n\n\n\nCliquez sur Choisir les fichiers à ajouter et\nnaviguez jusqu'à l'emplacement des\nensembles de données Slicer", - "0_TextBox_2": "30/06/2025", - "3_ArrowText_0": "Cliquez sur Welcome to Slicer dans le menu des modules et naviguez jusqu’à la catégorie Segmentation", - "4_ArrowText_0": "Saisissez le nom du modèle Prostate dans le menu du modèle de Segmentation", - "5_ArrowText_0": "Saisissez le volume T2 d’entrée msdprostate-01-adc", - "6_ArrowText_0": "Et le volume ADC d’entrée msd-prostate-01-adc", - "15_TextBox_0": "Slicer démarre l’inférence", - "9_TextBox_0": "Chargez dataset4_BrainMRI_Glioma", - "10_ArrowText_0": "Sélectionnez le module MONAIAuto3DSeg et entrez \nle nom du modèle, Brain Tumor Segmentation, dans le\n menu Segmentation model", - "13_ArrowText_1": "Une fois la segmentation terminée, ‘Processing finished’ apparaît dans l’interface de Slicer", - "15_ArrowText_0": "Sélectionnez le module MONAIAuto3DSeg et saisissez le nom du modèle Whole Body Segmentation dans le menu du modèle de Segmentation) ;", - "15_ArrowText_1": "Sélectionnez le module MONAIAuto3DSeg et saisissez le nom du modèle Whole Body Segmentation dans le menu du modèle de Segmentation) ;", - "18_TextBox_0": "Slicer affiche le résultat de la segmentation basée sur l’IA \nen utilisant le modèle Whole Body Segmentation TS1-quick", + "30_TextBox_1": "The 3D Slicer MONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", "31_TextBox_0": "Remerciements", - "13_0_TextBox_0": "Saisissez les volumes d'entrée comme suit :", - "16_2_TextBox_0": "Dans le module Add DICOM Data,\nsélectionnez le patient patient1 et\ncliquez sur charger pour le charger dans Slicer", - "17_0_TextBox_0": "Sélectionnez le module MONAIAuto3DSeg et entrez \nle nom du modèle, Whole Body Segmentation, dans le \n\nmenu Segmentation model", - "17_0_ArrowText_1": "Sélectionnez le modèle Whole Body Segmentation\nTS1 - rapide", - "18_0_TextBox_3": "Sélectionnez le volume d'entrée 6 : CT_Thorax_Abdomen,\n\nCliquez sur Créer une nouvelle segmentation sur Appliquer\nCliquez sur Appliquer pour démarrer la segmentation", - "19_0_TextBox_0": "Slicer commence la segmentation", - "20_0_TextBox_1": "Cliquez sur Afficher en 3D pour voir\n\nla vue 3D des segments" + "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible through funding by the Chan Zuckerberg Initiative." } \ No newline at end of file diff --git a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_pt-BR.json b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_pt-BR.json index 9ffb2974..3f45b344 100644 --- a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_pt-BR.json +++ b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_pt-BR.json @@ -3,47 +3,47 @@ "0_TextBox_1": "Sonia Pujol, Ph. D\nBrigham and Women's Hospital ,\nEscola de Medicina de Harvard\nBoston, MA", "0_TextBox_3": "Oficina de Slicer Ribeirão Preto\n30 de junho de 2025", "1_TextBox_0": "Manual vs AI-powered Segmentation", + "2_TextBox_0": "Manual vs AI-powered Segmentation", "1_TextBox_1": "As imagens médicas têm sido tradicionalmente segmentadas manualmente, o que é um processo demorado que requer esforço intensivo de radiologistas e está sujeito à variabilidade inter-reader.", - "2_TextBox_0": "Manual vs Segmentação por IA", "2_TextBox_1": "Na última década, a segmentação de imagem tem sido alimentada pelo desenvolvimento de algoritmos de aprendizagem profunda (por exemplo, nnUnet pelo Centro Alemão de Pesquisa de Câncer (DKFZ)/Helmholtz Research).\nAs ferramentas de segmentação alimentadas por IA podem reduzir o tempo de segmentação e proporcionar resultados mais reprodutíveis.", "3_TextBox_0": "Terminologia", - "3_TextBox_1": "A is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a Model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", - "4_TextBox_0": "3D Slicer AI Workshop", - "4_TextBox_1": "This tutorial focuses on running inference tasks using various pretrained AI models for automated segmentation of anatomical and pathological structures.", + "3_TextBox_1": "A Model is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", + "4_TextBox_0": "3D Slicer AI Tutorial", + "4_TextBox_1": "This tutorial focuses on running inference tasks using various pre-trained AI models for automated segmentation of anatomical and pathological structures.", "5_TextBox_0": "MONAIAuto3DSeg Slicer extension", - "5_TextBox_1": "This tutorial uses the pre-trained models of the MONAIAuto3DSeg Slicer extension.\n\n\nThe tool is designed to work on laptops or on average desktop computer without a GPU.", "6_TextBox_0": "MONAIAuto3DSeg Slicer extension", + "5_TextBox_1": "This tutorial uses the pre-trained models of the MONAIAuto3DSeg Slicer extension.\n\n\nThe tool is designed to work on laptops or on average desktop computer without a GPU.", "6_TextBox_1": "Multiple modalities Support (CT, MRI).\n\n\nMultiple anatomies (head, thorax, abdomen, pelvis, etc.).\n\n\nMultiple pathologies (tumor, hemorrhage, edema).", "7_TextBox_0": "Slicer AI Tutorial: Segmentation Tasks", - "7_TextBox_1": "Segmentation Task #1: Prostate.\n\n\nSegmentation Task #2: Brain Glioma.\n\n\nSegmentation Task #3: Whole Body Segmentation.", + "7_TextBox_1": "Segmentation Task #1: Prostate \n\n\nSegmentation Task #2: Brain Glioma \n\n\nSegmentation Task #3: Whole Body Segmentation", "8_TextBox_0": "AI Segmentation Task #1: Prostate", "9_TextBox_1": "AI-based Segmentation of Peripheral Zone (PZ) and Transition Zone (TZ) of the prostate on T2-weighted MRI Images.\n\n\nDataset:\nmsd_prostate_01-t2\nmsd_prostate_01-adc", - "10_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose Directory to Add and browse to the location of the Slicer datasets", - "11_TextBox_0": "Slicer loads the prostate MRI data set", - "12_TextBox_0": "Click on Welcome to Slicer in the Modules' menu and browse to the category Segmentation\n\nSelec the MONAI Auto3DSeg module", + "10_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose Directory to Add and browse to the location of the Slicer datasets\n\nSelect the dataset3_ProstateMRI and click on Open", + "11_TextBox_0": "Slicer loads the prostate MRI dataset", + "12_TextBox_0": "Click on Welcome to Slicer in the Modules' menu and browse to the category Segmentation\n\nSelect the MONAIAuto3DSeg module", "13_TextBox_0": "Enter the model's name Prostate in the Segmentation model menu", "13_TextBox_1": "Select the model Prostate - Multisequence", - "14_TextBox_0": "Enter the Input T2 volume msd-prostate-01-adc and the Input ADC volume msd-prostate-01-adc.", + "14_TextBox_0": "Enter the Input T2 volume msd-prostate-01-t2 and the Input ADC volume msd-prostate-01-adc", "14_TextBox_1": "Click on Create new segmentation on Apply", "15_TextBox_0": "Slicer starts the inference", "16_TextBox_0": "Slicer shows the results of the AI-based prostate segmentation", "17_TextBox_0": "AI Segmentation Task #2: Brain Glioma", "18_TextBox_1": "AI-based Segmentation of Neoplasm, Necrosis and Edema in Brain MRI images.\n\n\nDatasets:\n1) BraTS-GLI_00005-000-t1n (T1-weighted)\n2) BraTS-GLI_00005-000-t1c (T1-weighted post-Gd)\n3) BraTS-GLI_00005-000-t2w (T2-weighted)\n4) BraTS-GLI_00005-000-t2f (T2-FLAIR )", - "19_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose File(s) to Add and browse to the loccation of the Slicer datasets", + "19_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose File(s) to Add and browse to the location of the Slicer datasets\n\nIn the subdirectory dataset4_BrainMRI_Glioma, select the four datasets BraTS-GLI-00006-t1c.nii.gz, BraTS-GLI-00006-t1n.nii.gz, BraTS-GLI-00006-t2f.nii.gz, BraTS-GLI-00006-t2w.nii.gz\n\nClick on Open", "20_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Brain Tumor Segmentation in the Segmentation model menu", "20_TextBox_1": "Select the model Brain Tumor Segmentation (BRATS) GLI", - "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t12f\nInput T1C volume: BraTS-GLI_00005-00-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Appply\n\nClick on Apply to start the segmentation", + "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t2f\nInput T1C volume: BraTS-GLI_00005-000-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", "22_TextBox_1": "Slicer starts running the inference task\n\nOnce the segmentation is done, 'Processing finished' appears in the Slicer GUI", "23_TextBox_1": "Click on Show 3D to display the 3D segments in the 3D Viewer", "24_TextBox_0": "AI Segmentation Task #3: Whole Body Segmentation", "25_TextBox_1": "AI-based Segmentation of the whole body.\n\n\nDataset:\nCT_ThoraxAbdomen", "26_TextBox_0": "In the Add DICOM Data module, select the Patient patient1 and double click onthe image CT_Thorax_Abdomen to load it in Slicer", - "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", + "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", "27_TextBox_1": "Select the model Whole Body Segmentation TS1-quick", - "28_TextBox_0": "Select the input Volume 6: CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", - "29_TextBox_0": "Slicer displays the results of the IA-based segmentation using the Whole Body Segmentation TS1-quick", + "28_TextBox_0": "Select the input Volume 6:CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", + "29_TextBox_0": "Slicer displays the results of the AI-based segmentation using the Whole Body Segmentation TS1-quick", "30_TextBox_0": "Conclusion", - "30_TextBox_1": "The 3D SlicerMONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", + "30_TextBox_1": "The 3D Slicer MONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", "31_TextBox_0": "Agradecimentos", - "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible by two CZI Essential Open Source Software for Science (EOSS cycle 4 & 5) grants." + "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible through funding by the Chan Zuckerberg Initiative." } \ No newline at end of file diff --git a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_ta.json b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_ta.json index 9390d6bf..66a9ff1e 100644 --- a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_ta.json +++ b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_ta.json @@ -1,68 +1,49 @@ { - "1_0_ArrowText_0": "வெல்கம் என்பதில் டேட்டாவைச் சேர் என்பதைக் சொடுக்கு செய்யவும் \nச்லைசர் தொகுதிக்கு", - "2_2_TextBox_1": "சேர்க்க கோப்பகத்தைத் தேர்ந்தெடு என்பதைக் சொடுக்கு செய்யவும் \nமற்றும் இருப்பிடத்தை உலாவவும் \nச்லைசர் தரவுத்தொகுப்புகள்", - "3_0_TextBox_0": "ச்லைசர் புரோச்டேட் எம்ஆர்ஐயை ஏற்றுகிறது \nதரவுத்தொகுப்பு", - "4_3_TextBox_1": "இதில் வெல்கம் டு ச்லைசரை சொடுக்கு செய்யவும் \nதொகுதிகள் பட்டியல் மற்றும் உலாவவும் \n\nவகை பிரிவு. \n\nMONAI Auto3DSeg ஐத் தேர்ந்தெடுக்கவும் \nதொகுதி", - "5_0_ArrowText_0": "மாடல்களின் பெயரை புரோச்டேட் உள்ளிடவும் \nபிரிவு மாதிரி பட்டியல்", "0_TextBox_0": "AI-based Segmentation in 3D Slicer", - "0_TextBox_1": "Sonia Pujol, Ph. D\nBrigham and Women's Hospital,\nHarvard Medical School\nBoston, MA", + "0_TextBox_1": "Sonia Pujol, Ph. D. \nBrigham and Women's Hospital,\nHarvard Medical School\nBoston, MA", "0_TextBox_3": "Slicer Ribeirão Preto Workshop\nJune 30, 2025", "1_TextBox_0": "Manual vs AI-powered Segmentation", "2_TextBox_0": "Manual vs AI-powered Segmentation", "1_TextBox_1": "Medical images have traditionally been manually segmented, which is a time-consuming process that requires intensive effort by radiologists and is subject to inter-reader variability.", "2_TextBox_1": "In the past decade, image segmentation has been powered by the development of deep learning algorithms (e.g. nnUnet by the German Cancer Research Center (DKFZ)/Helmholtz Research).\n\n\nAI-powered segmentation tools can reduce the segmentation time and provide more reproducible results.", "3_TextBox_0": "AI Terminology", - "3_TextBox_1": "A is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a Model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", - "4_TextBox_0": "3D Slicer AI Workshop", - "4_TextBox_1": "This tutorial focuses on running inference tasks using various pretrained AI models for automated segmentation of anatomical and pathological structures.", + "3_TextBox_1": "A Model is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", + "4_TextBox_0": "3D Slicer AI Tutorial", + "4_TextBox_1": "This tutorial focuses on running inference tasks using various pre-trained AI models for automated segmentation of anatomical and pathological structures.", "5_TextBox_0": "MONAIAuto3DSeg Slicer extension", "6_TextBox_0": "MONAIAuto3DSeg Slicer extension", "5_TextBox_1": "This tutorial uses the pre-trained models of the MONAIAuto3DSeg Slicer extension.\n\n\nThe tool is designed to work on laptops or on average desktop computer without a GPU.", "6_TextBox_1": "Multiple modalities Support (CT, MRI).\n\n\nMultiple anatomies (head, thorax, abdomen, pelvis, etc.).\n\n\nMultiple pathologies (tumor, hemorrhage, edema).", "7_TextBox_0": "Slicer AI Tutorial: Segmentation Tasks", - "7_TextBox_1": "Segmentation Task #1: Prostate.\n\n\nSegmentation Task #2: Brain Glioma.\n\n\nSegmentation Task #3: Whole Body Segmentation.", + "7_TextBox_1": "Segmentation Task #1: Prostate \n\n\nSegmentation Task #2: Brain Glioma \n\n\nSegmentation Task #3: Whole Body Segmentation", "8_TextBox_0": "AI Segmentation Task #1: Prostate", "9_TextBox_1": "AI-based Segmentation of Peripheral Zone (PZ) and Transition Zone (TZ) of the prostate on T2-weighted MRI Images.\n\n\nDataset:\nmsd_prostate_01-t2\nmsd_prostate_01-adc", "10_TextBox_1": "மூளை கட்டி பிரிவின் மாதிரியைத் தேர்ந்தெடுக்கவும் \n(பிராட்ச்) சி.எல்.ஐ", - "11_TextBox_0": "Slicer loads the prostate MRI data set", - "12_TextBox_0": "Click on Welcome to Slicer in the Modules' menu and browse to the category Segmentation\n\nSelec the MONAI Auto3DSeg module", + "11_TextBox_0": "Slicer loads the prostate MRI dataset", + "12_TextBox_0": "Click on Welcome to Slicer in the Modules' menu and browse to the category Segmentation\n\nSelect the MONAIAuto3DSeg module", "13_TextBox_0": "Enter the model's name Prostate in the Segmentation model menu", "13_TextBox_1": "ப்ரோச்டேட் - மல்டிசீக்வென்ச் மாதிரியைத் தேர்ந்தெடுக்கவும்", - "14_TextBox_0": "Enter the Input T2 volume msd-prostate-01-adc and the Input ADC volume msd-prostate-01-adc.", + "14_TextBox_0": "Enter the Input T2 volume msd-prostate-01-t2 and the Input ADC volume msd-prostate-01-adc", "14_TextBox_1": "Click on Create new segmentation on Apply", + "15_TextBox_0": "Slicer starts the inference", "16_TextBox_0": "Slicer shows the results of the AI-based prostate segmentation", "17_TextBox_0": "AI Segmentation Task #2: Brain Glioma", "18_TextBox_1": "AI-based Segmentation of Neoplasm, Necrosis and Edema in Brain MRI images.\n\n\nDatasets:\n1) BraTS-GLI_00005-000-t1n (T1-weighted)\n2) BraTS-GLI_00005-000-t1c (T1-weighted post-Gd)\n3) BraTS-GLI_00005-000-t2w (T2-weighted)\n4) BraTS-GLI_00005-000-t2f (T2-FLAIR )", - "19_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose File(s) to Add and browse to the loccation of the Slicer datasets", + "19_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose File(s) to Add and browse to the location of the Slicer datasets\n\nIn the subdirectory dataset4_BrainMRI_Glioma, select the four datasets BraTS-GLI-00006-t1c.nii.gz, BraTS-GLI-00006-t1n.nii.gz, BraTS-GLI-00006-t2f.nii.gz, BraTS-GLI-00006-t2w.nii.gz\n\nClick on Open", "20_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Brain Tumor Segmentation in the Segmentation model menu", "20_TextBox_1": "Select the model Brain Tumor Segmentation (BRATS) GLI", - "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t12f\nInput T1C volume: BraTS-GLI_00005-00-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Appply\n\nClick on Apply to start the segmentation", + "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t2f\nInput T1C volume: BraTS-GLI_00005-000-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", "22_TextBox_1": "Slicer starts running the inference task\n\nOnce the segmentation is done, 'Processing finished' appears in the Slicer GUI", "23_TextBox_1": "Click on Show 3D to display the 3D segments in the 3D Viewer", "24_TextBox_0": "AI Segmentation Task #3: Whole Body Segmentation", "25_TextBox_1": "AI-based Segmentation of the whole body.\n\n\nDataset:\nCT_ThoraxAbdomen", "26_TextBox_0": "In the Add DICOM Data module, select the Patient patient1 and double click onthe image CT_Thorax_Abdomen to load it in Slicer", - "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", + "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", "27_TextBox_1": "Select the model Whole Body Segmentation TS1-quick", - "28_TextBox_0": "Select the input Volume 6: CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", - "29_TextBox_0": "Slicer displays the results of the IA-based segmentation using the Whole Body Segmentation TS1-quick", + "28_TextBox_0": "Select the input Volume 6:CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", + "29_TextBox_0": "Slicer displays the results of the AI-based segmentation using the Whole Body Segmentation TS1-quick", "30_TextBox_0": "Conclusion", - "30_TextBox_1": "The 3D SlicerMONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", - "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible by two CZI Essential Open Source Software for Science (EOSS cycle 4 & 5) grants.", - "7_0_TextBox_0": "உள்ளீடு T2 தொகுதி msd-ஐ உள்ளிடவும் \nprostate-01-adc மற்றும் he Input ADC \nதொகுதி msd-prostate-01-adc", - "7_0_ArrowText_1": "புதிய பிரிவை உருவாக்கு என்பதைத் தேர்ந்தெடுக்கவும் \nவிண்ணப்பிக்கவும்", - "7_0_ArrowText_2": "விண்ணப்பிக்கவும் என்பதைக் சொடுக்கு செய்யவும்", - "8_0_TextBox_0": "ச்லைசர் அனுமானத்தை நட்சத்திரமாக்குகிறது", - "9_0_TextBox_0": "ச்லைசர் AI இன் முடிவைக் காட்டுகிறது. \nஅடிப்படையிலான புரோச்டேட் பிரிவு", - "10_2_TextBox_2": "வெல்கம் என்பதில் டேட்டாவைச் சேர் என்பதைக் சொடுக்கு செய்யவும் \nச்லைசர் தொகுதிக்கு \n\n\n\n\n\n\n\n\n\n\nசேர்ப்பதற்கு கோப்பு(களை) தேர்ந்தெடு என்பதைக் சொடுக்கு செய்யவும் \nச்லைசரின் இடத்திற்கு உலாவவும் \nதரவுத்தொகுப்புகள்", - "15_TextBox_0": "Slicer starts the inference", - "10_ArrowText_0": "MONAIAauto3DSeg தொகுதியைத் தேர்ந்தெடுத்து உள்ளிடவும் \nமாடலின் பெயர் Brain Tumor Segmentation இல் \nபிரிவு மாதிரி பட்டியல்", + "30_TextBox_1": "The 3D Slicer MONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", "31_TextBox_0": "Acknowledgements", - "13_0_TextBox_0": "உள்ளீட்டு தொகுதிகளை பின்வருமாறு உள்ளிடவும்:", - "16_2_TextBox_0": "சேர் DICOM தரவு தொகுதியில், \nநோயாளி நோயாளி1 மற்றும் \nச்லைசரில் ஏற்றுவதற்கு ஏற்ற என்பதைக் சொடுக்கு செய்யவும்", - "17_0_TextBox_0": "MONAIAauto3DSeg தொகுதியைத் தேர்ந்தெடுத்து உள்ளிடவும் \nமுழு உடல் பிரிவு மாடலின் பெயர் \n\nபிரிவு மாதிரிப் பட்டியல்", - "17_0_ArrowText_1": "முழு உடல் பிரிவின் மாதிரியைத் தேர்ந்தெடுக்கவும் \nTS1 - விரைவானது", - "18_0_TextBox_3": "உள்ளீடு தொகுதி 6 ஐத் தேர்ந்தெடுக்கவும்: CT_Thorax_Abdomen, \n\nApply என்பதில் Create புதிய Segmentation என்பதில் சொடுக்கு செய்யவும் \nபிரிவைத் தொடங்க விண்ணப்பிக்கவும் என்பதைக் சொடுக்கு செய்யவும்", - "19_0_TextBox_0": "ச்லைசர் பிரிவைத் தொடங்குகிறது", - "20_0_TextBox_1": "பார்க்க 3D ஐக் சொடுக்கு செய்க \n\nபிரிவுகள் 3D காட்சி" + "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible through funding by the Chan Zuckerberg Initiative." } \ No newline at end of file diff --git a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_th.json b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_th.json index 656bbc2b..f7d01c66 100644 --- a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_th.json +++ b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_th.json @@ -1,50 +1,49 @@ { - "1_0_ArrowText_0": "คลิกเพิ่มข้อมูลในหน้ายินดีต้อนรับ\nเพื่อไปที่โมดูล Slicer", "0_TextBox_0": "AI-based Segmentation in 3D Slicer", - "0_TextBox_1": "Sonia Pujol, Ph. D\nBrigham and Women's Hospital,\nHarvard Medical School\nBoston, MA", + "0_TextBox_1": "Sonia Pujol, Ph. D. \nBrigham and Women's Hospital,\nHarvard Medical School\nBoston, MA", "0_TextBox_3": "Slicer Ribeirão Preto Workshop\nJune 30, 2025", "1_TextBox_0": "Manual vs AI-powered Segmentation", "2_TextBox_0": "Manual vs AI-powered Segmentation", "1_TextBox_1": "Medical images have traditionally been manually segmented, which is a time-consuming process that requires intensive effort by radiologists and is subject to inter-reader variability.", "2_TextBox_1": "In the past decade, image segmentation has been powered by the development of deep learning algorithms (e.g. nnUnet by the German Cancer Research Center (DKFZ)/Helmholtz Research).\n\n\nAI-powered segmentation tools can reduce the segmentation time and provide more reproducible results.", "3_TextBox_0": "AI Terminology", - "3_TextBox_1": "A is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a Model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", - "4_TextBox_0": "3D Slicer AI Workshop", - "4_TextBox_1": "This tutorial focuses on running inference tasks using various pretrained AI models for automated segmentation of anatomical and pathological structures.", + "3_TextBox_1": "A Model is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", + "4_TextBox_0": "3D Slicer AI Tutorial", + "4_TextBox_1": "This tutorial focuses on running inference tasks using various pre-trained AI models for automated segmentation of anatomical and pathological structures.", "5_TextBox_0": "MONAIAuto3DSeg Slicer extension", "6_TextBox_0": "MONAIAuto3DSeg Slicer extension", "5_TextBox_1": "This tutorial uses the pre-trained models of the MONAIAuto3DSeg Slicer extension.\n\n\nThe tool is designed to work on laptops or on average desktop computer without a GPU.", "6_TextBox_1": "Multiple modalities Support (CT, MRI).\n\n\nMultiple anatomies (head, thorax, abdomen, pelvis, etc.).\n\n\nMultiple pathologies (tumor, hemorrhage, edema).", "7_TextBox_0": "Slicer AI Tutorial: Segmentation Tasks", - "7_TextBox_1": "Segmentation Task #1: Prostate.\n\n\nSegmentation Task #2: Brain Glioma.\n\n\nSegmentation Task #3: Whole Body Segmentation.", + "7_TextBox_1": "Segmentation Task #1: Prostate \n\n\nSegmentation Task #2: Brain Glioma \n\n\nSegmentation Task #3: Whole Body Segmentation", "8_TextBox_0": "AI Segmentation Task #1: Prostate", "9_TextBox_1": "AI-based Segmentation of Peripheral Zone (PZ) and Transition Zone (TZ) of the prostate on T2-weighted MRI Images.\n\n\nDataset:\nmsd_prostate_01-t2\nmsd_prostate_01-adc", - "10_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose Directory to Add and browse to the location of the Slicer datasets", - "11_TextBox_0": "Slicer loads the prostate MRI data set", - "12_TextBox_0": "Click on Welcome to Slicer in the Modules' menu and browse to the category Segmentation\n\nSelec the MONAI Auto3DSeg module", + "10_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose Directory to Add and browse to the location of the Slicer datasets\n\nSelect the dataset3_ProstateMRI and click on Open", + "11_TextBox_0": "Slicer loads the prostate MRI dataset", + "12_TextBox_0": "Click on Welcome to Slicer in the Modules' menu and browse to the category Segmentation\n\nSelect the MONAIAuto3DSeg module", "13_TextBox_0": "Enter the model's name Prostate in the Segmentation model menu", "13_TextBox_1": "Select the model Prostate - Multisequence", - "14_TextBox_0": "Enter the Input T2 volume msd-prostate-01-adc and the Input ADC volume msd-prostate-01-adc.", + "14_TextBox_0": "Enter the Input T2 volume msd-prostate-01-t2 and the Input ADC volume msd-prostate-01-adc", "14_TextBox_1": "Click on Create new segmentation on Apply", + "15_TextBox_0": "Slicer starts the inference", "16_TextBox_0": "Slicer shows the results of the AI-based prostate segmentation", "17_TextBox_0": "AI Segmentation Task #2: Brain Glioma", "18_TextBox_1": "AI-based Segmentation of Neoplasm, Necrosis and Edema in Brain MRI images.\n\n\nDatasets:\n1) BraTS-GLI_00005-000-t1n (T1-weighted)\n2) BraTS-GLI_00005-000-t1c (T1-weighted post-Gd)\n3) BraTS-GLI_00005-000-t2w (T2-weighted)\n4) BraTS-GLI_00005-000-t2f (T2-FLAIR )", - "19_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose File(s) to Add and browse to the loccation of the Slicer datasets", + "19_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose File(s) to Add and browse to the location of the Slicer datasets\n\nIn the subdirectory dataset4_BrainMRI_Glioma, select the four datasets BraTS-GLI-00006-t1c.nii.gz, BraTS-GLI-00006-t1n.nii.gz, BraTS-GLI-00006-t2f.nii.gz, BraTS-GLI-00006-t2w.nii.gz\n\nClick on Open", "20_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Brain Tumor Segmentation in the Segmentation model menu", "20_TextBox_1": "Select the model Brain Tumor Segmentation (BRATS) GLI", - "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t12f\nInput T1C volume: BraTS-GLI_00005-00-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Appply\n\nClick on Apply to start the segmentation", + "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t2f\nInput T1C volume: BraTS-GLI_00005-000-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", "22_TextBox_1": "Slicer starts running the inference task\n\nOnce the segmentation is done, 'Processing finished' appears in the Slicer GUI", "23_TextBox_1": "Click on Show 3D to display the 3D segments in the 3D Viewer", "24_TextBox_0": "AI Segmentation Task #3: Whole Body Segmentation", "25_TextBox_1": "AI-based Segmentation of the whole body.\n\n\nDataset:\nCT_ThoraxAbdomen", "26_TextBox_0": "In the Add DICOM Data module, select the Patient patient1 and double click onthe image CT_Thorax_Abdomen to load it in Slicer", - "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", + "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", "27_TextBox_1": "Select the model Whole Body Segmentation TS1-quick", - "28_TextBox_0": "Select the input Volume 6: CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", - "29_TextBox_0": "Slicer displays the results of the IA-based segmentation using the Whole Body Segmentation TS1-quick", + "28_TextBox_0": "Select the input Volume 6:CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", + "29_TextBox_0": "Slicer displays the results of the AI-based segmentation using the Whole Body Segmentation TS1-quick", "30_TextBox_0": "Conclusion", - "30_TextBox_1": "The 3D SlicerMONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", - "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible by two CZI Essential Open Source Software for Science (EOSS cycle 4 & 5) grants.", - "15_TextBox_0": "Slicer starts the inference", - "31_TextBox_0": "Acknowledgements" + "30_TextBox_1": "The 3D Slicer MONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", + "31_TextBox_0": "Acknowledgements", + "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible through funding by the Chan Zuckerberg Initiative." } \ No newline at end of file diff --git a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_uk.json b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_uk.json index af78e34d..5e18e66a 100644 --- a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_uk.json +++ b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_uk.json @@ -1,26 +1,21 @@ { - "1_0_ArrowText_0": "Натисніть кнопку «Додати дані»\nв модулі «Ласкаво просимо до зрізу»", - "2_2_TextBox_1": "Натисніть «Вибрати каталог для додавання»\nта перейдіть до розташування наборів\nданих Slicer", - "3_0_TextBox_0": "Слайсер завантажує дані МР\n простати", - "4_3_TextBox_1": "Натисніть «Ласкаво просимо до Slicer» у\nменю «Модулі» та перейдіть до\n\nкатегорії «Сегментація».\n\nВиберіть модуль MONAI\nAuto3DSeg", - "5_0_ArrowText_0": "Введіть назву моделі «Простата» в\nменю «Модель сегментації»", "0_TextBox_0": "AIBasedSegmentationIn3DSlicer", "0_TextBox_1": "Соня Пуйоль, доктор філ.", "0_TextBox_3": "AI - Сегментація на основі 3D Slicer", "1_TextBox_0": "Manual vs AI-powered Segmentation", - "2_TextBox_0": "Slicer завантажує набір даних МРТ простати", + "2_TextBox_0": "Manual vs AI-powered Segmentation", "1_TextBox_1": "Натисніть кнопку «Додати дані» у вікні привітання \tМодуль Slicer", "2_TextBox_1": "In the past decade, image segmentation has been powered by the development of deep learning algorithms (e.g. nnUnet by the German Cancer Research Center (DKFZ)/Helmholtz Research).\n\n\nAI-powered segmentation tools can reduce the segmentation time and provide more reproducible results.", "3_TextBox_0": "AI Terminology", - "3_TextBox_1": "A is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a Model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", - "4_TextBox_0": "3D Slicer AI Workshop", - "4_TextBox_1": "This tutorial focuses on running inference tasks using various pretrained AI models for automated segmentation of anatomical and pathological structures.", + "3_TextBox_1": "A Model is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", + "4_TextBox_0": "3D Slicer AI Tutorial", + "4_TextBox_1": "This tutorial focuses on running inference tasks using various pre-trained AI models for automated segmentation of anatomical and pathological structures.", "5_TextBox_0": "MONAIAuto3DSeg Slicer extension", "6_TextBox_0": "MONAIAuto3DSeg Slicer extension", "5_TextBox_1": "This tutorial uses the pre-trained models of the MONAIAuto3DSeg Slicer extension.\n\n\nThe tool is designed to work on laptops or on average desktop computer without a GPU.", "6_TextBox_1": "Натисніть кнопку «Створити нову сегментацію» на сторінці\n«Застосувати»", "7_TextBox_0": "Slicer AI Tutorial: Segmentation Tasks", - "7_TextBox_1": "Segmentation Task #1: Prostate.\n\n\nSegmentation Task #2: Brain Glioma.\n\n\nSegmentation Task #3: Whole Body Segmentation.", + "7_TextBox_1": "Segmentation Task #1: Prostate \n\n\nSegmentation Task #2: Brain Glioma \n\n\nSegmentation Task #3: Whole Body Segmentation", "8_TextBox_0": "Slicer показує результат сегментації простати на основі штучного інтелекту", "9_TextBox_1": "AI-based Segmentation of Peripheral Zone (PZ) and Transition Zone (TZ) of the prostate on T2-weighted MRI Images.\n\n\nDataset:\nmsd_prostate_01-t2\nmsd_prostate_01-adc", "10_TextBox_1": "Виберіть модель сегментації пухлини головного мозку\n(BRATS) GLI", @@ -30,49 +25,25 @@ "13_TextBox_1": "Виберіть модель Простата - Мультисеквенція", "14_TextBox_0": "У модулі «Додати дані DICOM»\nвиберіть пацієнта patient1 та\nдвічі клацніть на зображенні\nCT_Thorax_Abdomen, щоб завантажити його в\nSlicer", "14_TextBox_1": "Click on Create new segmentation on Apply", + "15_TextBox_0": "Slicer починає висновок", "16_TextBox_0": "Виберіть вхідний том 6: КТ_Грудної_черевної_плоди,\nНатисніть «Створити нову сегментацію» на кнопці «Застосувати»\nНатисніть «Застосувати», щоб розпочати сегментацію", "17_TextBox_0": "AI Segmentation Task #2: Brain Glioma", "18_TextBox_1": "AI-based Segmentation of Neoplasm, Necrosis and Edema in Brain MRI images.\n\n\nDatasets:\n1) BraTS-GLI_00005-000-t1n (T1-weighted)\n2) BraTS-GLI_00005-000-t1c (T1-weighted post-Gd)\n3) BraTS-GLI_00005-000-t2w (T2-weighted)\n4) BraTS-GLI_00005-000-t2f (T2-FLAIR )", - "19_TextBox_1": "Click on Add Data in the Welcome to Slicer Module\n\nClick on Choose File(s) to Add and browse to the loccation of the Slicer datasets", + "19_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose File(s) to Add and browse to the location of the Slicer datasets\n\nIn the subdirectory dataset4_BrainMRI_Glioma, select the four datasets BraTS-GLI-00006-t1c.nii.gz, BraTS-GLI-00006-t1n.nii.gz, BraTS-GLI-00006-t2f.nii.gz, BraTS-GLI-00006-t2w.nii.gz\n\nClick on Open", "20_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Brain Tumor Segmentation in the Segmentation model menu", "20_TextBox_1": "Select the model Brain Tumor Segmentation (BRATS) GLI", - "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t12f\nInput T1C volume: BraTS-GLI_00005-00-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Appply\n\nClick on Apply to start the segmentation", + "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t2f\nInput T1C volume: BraTS-GLI_00005-000-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", "22_TextBox_1": "Slicer starts running the inference task\n\nOnce the segmentation is done, 'Processing finished' appears in the Slicer GUI", "23_TextBox_1": "Click on Show 3D to display the 3D segments in the 3D Viewer", "24_TextBox_0": "AI Segmentation Task #3: Whole Body Segmentation", "25_TextBox_1": "AI-based Segmentation of the whole body.\n\n\nDataset:\nCT_ThoraxAbdomen", "26_TextBox_0": "In the Add DICOM Data module, select the Patient patient1 and double click onthe image CT_Thorax_Abdomen to load it in Slicer", - "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", + "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", "27_TextBox_1": "Select the model Whole Body Segmentation TS1-quick", - "28_TextBox_0": "Select the input Volume 6: CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", - "29_TextBox_0": "Slicer displays the results of the IA-based segmentation using the Whole Body Segmentation TS1-quick", + "28_TextBox_0": "Select the input Volume 6:CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", + "29_TextBox_0": "Slicer displays the results of the AI-based segmentation using the Whole Body Segmentation TS1-quick", "30_TextBox_0": "Conclusion", - "30_TextBox_1": "The 3D SlicerMONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", - "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible by two CZI Essential Open Source Software for Science (EOSS cycle 4 & 5) grants.", - "7_0_TextBox_0": "Введіть об'єм вхідного T2 msd-\nprostate-01-adc та об'єм вхідного ADC\nmsd-prostate-01-adc", - "7_0_ArrowText_1": "Виберіть «Створити нову сегментацію» на\n«Застосувати»", - "7_0_ArrowText_2": "Натисніть кнопку «Застосувати»", - "8_0_TextBox_0": "Зрізач визначає висновок", - "9_0_TextBox_0": "Зріз показує результат сегментації\nпростати на основі штучного інтелекту", - "10_2_TextBox_2": "Натисніть кнопку «Додати дані» у вікні привітання\nдо модуля Slicer\n\n\n\n\n\n\n\n\n\n\nНатисніть кнопку «Вибрати файл(и) для додавання» та\nперейдіть до розташування зрізу\nнабори даних", - "0_TextBox_2": "30/06/2025", - "3_ArrowText_0": "Натисніть «Ласкаво просимо до Slicer» у\nменю «Модулі» та перейдіть до\nкатегорії «Сегментація».", - "4_ArrowText_0": "Введіть назву моделі «Простата» в\nменю «Модель сегментації»", - "5_ArrowText_0": "Введіть вхідний об'єм T2 msdprostate-01-adc", - "6_ArrowText_0": "І обсяг вхідного АЦП msd-prostate-01-adc", - "15_TextBox_0": "Slicer починає висновок", - "9_TextBox_0": "Завантаження dataset4_BrainMRI_Glioma", - "10_ArrowText_0": "Виберіть модуль MONAIAuto3DSeg і введіть\nназву моделі Brain Tumor Segmentation (Сегментація пухлини мозку) в\nменю Segmentation model (Модель сегментації)", - "13_ArrowText_1": "Після завершення сегментації в графічному інтерфейсі Slicer\nз’явиться напис «Обробку завершено»", - "15_ArrowText_0": "Виберіть модуль MONAIAuto3DSeg та введіть назву моделі\nWhole Body Segmentation (Сегментація всього тіла) у\nменю Модель сегментації);", - "15_ArrowText_1": "Виберіть модуль MONAIAuto3DSeg та введіть назву моделі\nWhole Body Segmentation (Сегментація всього тіла) у\nменю Модель сегментації);", - "18_TextBox_0": "Зріз відображає результат сегментації на основі штучного інтелекту\nза допомогою швидкої сегментації всього тіла TS1", + "30_TextBox_1": "The 3D Slicer MONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", "31_TextBox_0": "Подяки", - "13_0_TextBox_0": "Введіть вхідні обсяги наступним чином:", - "16_2_TextBox_0": "У модулі «Додати дані DICOM»\nвиберіть пацієнта patient1 і\nнатисніть «Завантажити», щоб завантажити його в Slicer", - "17_0_TextBox_0": "Виберіть модуль MONAIAuto3DSeg і введіть\nназву моделі «Сегментація всього тіла» в\n\nменю «Модель сегментації»", - "17_0_ArrowText_1": "Виберіть модель сегментації всього тіла\nTS1 – швидкий", - "18_0_TextBox_3": "Виберіть вхідний том 6: КТ_Грудної_клітини_Черевини,\n\nНатисніть «Створити нову сегментацію» на «Застосувати».\nНатисніть кнопку «Застосувати», щоб розпочати сегментацію", - "19_0_TextBox_0": "Slicer починає сегментацію", - "20_0_TextBox_1": "Натисніть кнопку «Показати 3D», щоб переглянути\n\n3D-вигляд сегментів" + "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible through funding by the Chan Zuckerberg Initiative." } \ No newline at end of file diff --git a/Tutorials/STC-VIS-101_VisualizationTutorial/Translations/text_dict_ar_SA.json b/Tutorials/STC-VIS-101_VisualizationTutorial/Translations/text_dict_ar_SA.json new file mode 100644 index 00000000..fafa50dc --- /dev/null +++ b/Tutorials/STC-VIS-101_VisualizationTutorial/Translations/text_dict_ar_SA.json @@ -0,0 +1,99 @@ +{ + "1_TextBox_1": "This tutorial is an introduction to the basics of loading and viewing DICOM images and 3D models in 3D Slicer.", + "3_TextBox_0": "Tutorial materials", + "4_TextBox_0": "Tutorial dataset", + "33_TextBox_0": "Tutorial dataset", + "5_TextBox_0": "Disclaimer", + "6_TextBox_0": "Tutorial Outline", + "8_LoadingaDICOMvolume_title": "Loading a DICOM volume", + "9_LoadingaDICOMvolume_title": "Loading a DICOM volume", + "8_TextBox_1": "The patient1 study contains a CT Thorax Abdomen dataset", + "11_ArrowText_2": "Select the module \nVolumes", + "10_VisualizingDICOMimages_title": "Visualizing DICOM images", + "11_VisualizingDICOMimages_title": "Visualizing DICOM images", + "12_VisualizingDICOMimages_title": "Visualizing DICOM images", + "13_VisualizingDICOMimages_title": "Visualizing DICOM images", + "14_VisualizingDICOMimages_title": "Visualizing DICOM images", + "15_VisualizingDICOMimages_title": "Visualizing DICOM images", + "16_VisualizingDICOMimages_title": "Visualizing DICOM images", + "17_VisualizingDICOMimages_title": "Visualizing DICOM images", + "18_VisualizingDICOMimages_title": "Visualizing DICOM images", + "16_TextBox_0": "Slicer switches the layout \nto Conventional \nWidescreen layout", + "17_TextBox_0": "Use the right mouse button in the \n3D Viewer to zoom in and out", + "19_3DViewerController_title": "3D Viewer Controller", + "20_3DViewerController_title": "3D Viewer Controller", + "22_TextBox_0": "Volume Rendering", + "23_VolumeRendering_title": "Volume Rendering", + "24_VolumeRendering_title": "Volume Rendering", + "25_VolumeRendering_title": "Volume Rendering", + "26_VolumeRendering_title": "Volume Rendering", + "27_VolumeRendering_title": "Volume Rendering", + "28_VolumeRendering_title": "Volume Rendering", + "29_VolumeRendering_title": "Volume Rendering", + "30_VolumeRendering_title": "Volume Rendering", + "31_VolumeRendering_title": "Volume Rendering", + "24_TextBox_2": "Select VTK GPU Ray Casting Rendering\nClick on the eye icon in the Volume tab to display \nthe volume rendered image in the 3D viewer", + "26_ArrowText_0": "Click on Display ROI to \ndisplay a region of interest \n(ROI) in the 3D viewer and \ncheck the option Enable", + "28_ArrowText_0": "Click on the eye icon to \ndisplay the volume rendered \nimage", + "29_TextBox_0": "Slicer displays the \nvolume rendered \nimage of the left kidney ", + "30_TextBox_0": "Extend the ROI to generate \na volume rendered image of \nthe right kidney", + "34_TextBox_0": "Slicer Scene", + "35_LoadingaScene_title": "Loading a Scene", + "35_TextBox_0": "Slicer displays a 3D \nsurface model of the \nhead and 2D MRI slices", + "36_Viewing3Dmodels_title": "Viewing 3D models", + "37_Viewing3Dmodels_title": "Viewing 3D models", + "38_Viewing3Dmodels_title": "Viewing 3D models", + "39_Viewing3Dmodels_title": "Viewing 3D models", + "40_Viewing3Dmodels_title": "Viewing 3D models", + "36_ArrowText_0": "Position the cursor over \nthe pin icon to reveal the \nslice menu and click on \nthe eye icon to display the \naxial slice in the 3D viewer", + "41_Interactingwith3Dmodels_title": "Interacting with 3D models", + "42_Interactingwith3Dmodels_title": "Interacting with 3D models", + "43_Interactingwith3Dmodels_title": "Interacting with 3D models", + "44_Interactingwith3Dmodels_title": "Interacting with 3D models", + "38_ArrowText_0": "Lower the opacity of \nthe Skin model using \nthe visibility slider", + "38_TextBox_1": "The skull bone and \neyeballs models appear \nthrough the skin", + "40_TextBox_2": "The white matter and \noptic nerve models \nappear through the skin", + "40_ArrowText_1": "Select the skull bone \nmodel and click on the \neye icon to turn off its \nvisibility", + "0_TextBox_0": "Basics of Data Loading and 3D Visualization in 3D Slicer", + "0_TextBox_1": "Author: Sonia Pujol, Ph.D.", + "0_TextBox_2": "24/11/2024", + "0_TextBox_3": "Assistant Professor of Radiology Brigham and Women’s Hospital Harvard Medical School", + "1_TextBox_0": "Overall Goal", + "2_TextBox_0": "Learning Objectives", + "2_TextBox_1": " • Following this tutorial, you will be able\n\n• to load and visualize DICOM images in Slicer\n\n• to perform volume rendering of CT data\n\n• to load and visualize 3D models reconstructed from MRI data", + "3_TextBox_1": "• 3D Slicer version 5.10\n\n• 3D VisualizationDataSet.zip", + "4_TextBox_1": "The file 3DVisualizationDataset.zip contains two directories:\n\n- dataset1_Thorax_Abdomen \n- dataset2_Head\n\nUnzip the file 3DVisualizationDataset.zip on your computer to access the datasets", + "5_TextBox_1": "• 3D Slicer is a free open source software application distributed under a BSD style license. \n\n\n• The software is not FDA approved or CE-Marked, and is for research use only.\nTutorial Outline", + "6_TextBox_1": "• Part 1: Loading and Viewing DICOM data\n\n• Part 2: Volume Rendering\n\n\n• Part 3: Loading and Viewing 3D models", + "7_TextBox_0": "Part 1: DICOM Data Loading", + "8_TextBox_0": "Slicer displays the user interface of the DICOM module", + "9_ArrowText_1": "Select patient1 and click on Load \nto load the dataset in Slicer", + "10_TextBox_0": "Slicer displays the axial, coronal \nand sagittal images of the CT \nThorax Abdomen dataset ", + "11_ArrowText_1": "Left click on DICOM to display \nthe list of modules of Slicer", + "12_ArrowText_0": "Click on the CT-abdomen \npreset to automatically adjust \nthe window/level display of the \nCT dataset", + "13_TextBox_1": "Position the mouse cursor \nover the red banner in the \nRed Viewer to display the slice \nmenu.\n\n\nClick on the Links icon to link \nthe slice controls across all \nSlice Viewers. \n\n\nClick on the Eye icon to\ndisplay the three anatomical \nslices in the 3D Viewer", + "14_TextBox_0": "The three anatomical slices \nappear in the 3D viewer.", + "15_ArrowText_0": "Click on the Slicer layout menu \nicon, and select the \nConventional Widescreen layout", + "18_TextBox_0": "Use the left mouse button in the \n3D Viewer to rotate the images", + "19_TextBox_1": "Position the mouse cursor over \nthe pin icon in the blue banner \nof the 3D viewer window to \ndisplay the 3DView controller\n\nClick on the second icon on the \ntop row of the 3DView \ncontroller to center the 3D view \non the scene", + "20_ArrowText_1": "Select the Volume Rendering \nmodule in the list of modules ", + "21_TextBox_0": "Part 2: Volume Rendering", + "22_TextBox_1": "• Volume rendering \ntechniques enable 3D \nvisualization of 3D \ndatasets\n\n• The Volume Rendering \nmodule in Slicer enables \ninteractive 3D visualization \nof DICOM images", + "23_ArrowText_0": "Click on Preset in the Display tab \nand select the preset CT-Cardiac3 ", + "25_ArrowText_0": "Use the shift slider to \nchange the transfer \nfunction and display the\naorta", + "27_TextBox_0": "Turn off the visibility of the \naxial, sagittal and coronal \nslices in the 2D viewer \n\n\nPosition the ROI around the \nleft kidney using the color \nhandles", + "31_ArrowText_1": "Click on File, then Close Scene\nin the main menu", + "32_TextBox_0": "Part 3: Loading and \nviewing 3D models\n", + "33_TextBox_1": "• The directory dataset2_Head contains the Slicer scene called Head_scene.mrb\n\n• The scene contains 3D models from the SPL brain atlas developed by the department of Radiology at Brigham and Women’s Hospital, Harvard Medical School (NIH P41 RR013218, NIH R01 MH05074)", + "34_TextBox_1": "Slicer stores all loaded data in a repository called a scene\n\n\nEach data set, such as an image volume, surface model, or point set, is represented as a node in a Slicer scene.\n\n\nAll Slicer modules operate on the data stored in a Slicer scene.", + "37_ArrowText_1": "Select the Models module \nin the list of models", + "39_ArrowText_0": "Slicer displays the list of \n3D models loaded in the \nscene\n\nSelect the model Skin.vtk", + "41_ArrowText_0": "Click on the eye icon to \ndisplay the coronal slice \nin the 3D viewer", + "42_ArrowText_2": "Select the hemispheric \nwhite matter model and \nselect the option Clipping", + "43_TextBox_0": "Move the coronal slice \nposteriorily to display the \noptic chiasm", + "44_TextBox_0": "Slicer displays a 3D view of \nthe optic chiasm", + "45_TextBox_1": "• 3D Slicer provides advanced functionalities for loading and viewing 3D medical imaging data\n\n• The tutorial demonstrates how to use volume \nrendering and 3D surface modeling for \ninteractive visualization of CT and MRI data\n\n\nContact: spujol@bwh.harvard.edu", + "46_TextBox_0": "Acknowledgements", + "46_TextBox_1": "Neuroimage Analysis Center (NIBIB P41 EB015902)", + "45_TextBox_0": "Conclusion" +} \ No newline at end of file