Update translations from SlicerLanguageTranslations

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",

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."
 }