diff --git a/LICENCE.txt b/LICENCE.txt index 10a843f28..20c0d9dd9 100644 --- a/LICENCE.txt +++ b/LICENCE.txt @@ -1,11 +1,7 @@ Copyright 2021 Macatools -Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: -1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. -2. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/paper_JOSS/paper.bib b/paper_JOSS/paper.bib new file mode 100644 index 000000000..0f0670398 --- /dev/null +++ b/paper_JOSS/paper.bib @@ -0,0 +1,135 @@ +@article{avants2011reproducible, title={ + A reproducible evaluation of ANTs similarity metric performance in brain image registration}, author={Avants, BB and Tustison, NJ and others}, + fullauthor={Avants, BB and Tustison, NJ and Song, G and Cook, PA and Klein, A and Gee, JC}, + journal={NeuroImage}, volume={54}, pages={2033--2044}, year={2011}, publisher={Elsevier}, doi={10.1016/j.neuroimage.2010.09.025} } + +@article{balbastre2017primatologist, title={ + Primatologist: A modular segmentation pipeline for macaque brain morphometry}, author={Balbastre, Y and others}, journal={NeuroImage}, volume={162}, pages={306--321}, year={2017}, publisher={Elsevier} , doi={10.1016/j.neuroimage.2017.09.007}} + +@article{cieslak2021qsiprep, title={ + QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data}, author={Cieslak, M and others}, journal={Nature methods}, volume={18}, number={7}, pages={775--778}, year={2021}, publisher={Nature Publishing Group} , doi={10.1038/s41592-021-01185-5}} + +@article{esteban2019fmriprep, +title={fMRIPrep: a robust preprocessing pipeline for functional MRI}, author={Esteban, O and Birman, D and others}, +fullauthor={Esteban, O and Birman, D and Schaer, M and Koyejo, OO and Poldrack, RA and Gorgolewski, KJ}, +journal={Nature methods}, volume={16}, number={1}, pages={111--116}, year={2019}, publisher={Nature Publishing Group} , doi={10.1038/s41592-018-0235-4} + } + +@article{cox1996afni, title={ + AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages}, author={Cox, RW}, journal={Computers and Biomedical Research}, volume={29}, pages={162--173}, year={1996}, publisher={Elsevier}, doi={10.1006/cbmr.1996.0014} + } + +@article{esteban2017mriqc, +title={ MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites}, +author={Esteban, O and Birman, D and others}, +fullauthor={Esteban, O and Birman, D and Schaer, M and Koyejo, OO and Poldrack, RA and Gorgolewski, KJ}, +journal={PLoS One}, volume={12}, pages={1--21}, year={2017}, publisher={Public Library of Science}, doi={10.1371/journal.pone.0184661} + } + +@article{fischl2012freesurfer, title={ + FreeSurfer}, author={Fischl, B}, journal={NeuroImage}, volume={62}, pages={774--781}, year={2012}, publisher={Elsevier}, doi={10.1016/j.neuroimage.2012.01.021} + } + +@book{frackowiak1997human, title={ + Human Brain Function}, author={Frackowiak, RSJ and Friston, KJ and Frith, CD and Dolan, RJ and Mazziotta, JC}, year={1997}, publisher={Academic Press USA} , doi={10.1027/0269-8803.14.2.128} + } + +@article{garcia2021preemacs, title={ + PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface}, author={Garcia-Saldivar, P and Garimella, A and others}, fullauthor={Garcia-Saldivar, P and Garimella, A and Garza-Villarreal, EA and Mendez, FA and Concha, L and Merchant, H}, journal={NeuroImage}, volume={227}, pages={117671}, year={2021}, publisher={Elsevier}, doi={10.1016/j.neuroimage.2020.117671} + } +@article{gorgolewski2011nipype, +title={Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python}, +author={Gorgolewski, K and Burns, Christopher and others}, journal={Front. 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doi={10.1016/j.neuroimage.2020.117575} + } + +@article{dice1945measures, title={ + Measures of the amount of ecologic association between species}, author={Dice, LR}, journal={Ecology}, volume={26}, number={3}, pages={297--302}, year={1945}, publisher={Wiley Online Library}, doi={10.2307/1932409} + } + +@article{cohen1960coefficient, title={ + A coefficient of agreement for nominal scales}, author={Cohen, J}, journal={Educ Psychol Meas}, volume={20}, pages={37--46}, year={1960}, publisher={Sage Publications}, doi={10.1177/001316446002000104} + } + +@phdthesis{martin2002empirical, title={ + An Empirical Approach to Grouping and Segmentation}, author={Martin, D}, school={University of California, Berkeley, CA, USA}, year={2002} , doi={10.5555/937222} + } + +@article{Wang2021unet, title={ + U-Net Model for Brain Extraction on Non-human Primates}, author={Wang, X and Li, X and Cho, J W and Russ, B and Rajamani, N and Omelchenko, A and Ai, L and Korchmaros, A and Garcia, P and Wang, Z and Kalin, N H and Schroeder, C E and Craddock, C and Fox, A S and Evans, A and Messinger, A and Milham, M P and & Xu, T }, journal={NeuroImage}, volume={235}, pages={118001}, year={2021} , doi={10.1016/j.neuroimage.2021.118001} + } + +@article{ZHONG2021117649,title = { + DIKA-Nets: Domain-invariant knowledge-guided attention networks for brain skull stripping of early developing macaques},journal = {NeuroImage},volume = {227},pages = {117649},year = {2021},issn = {1053-8119},doi = {10.1016/j.neuroimage.2020.117649},url = {https://www.sciencedirect.com/science/article/pii/S1053811920311344},author = {Tao Zhong and Fenqiang Zhao and Yuchen Pei and Zhenyuan Ning and Lufan Liao and Zhengwang Wu and Yuyu Niu and Li Wang and Dinggang Shen and Yu Zhang and Gang Li} + } + +@article{ZHONG2024120652,title = { + nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species},journal = {NeuroImage},volume = {295},pages = {120652},year = {2024},issn = {1053-8119},doi = {10.1016/j.neuroimage.2024.120652},url = {https://www.sciencedirect.com/science/article/pii/S1053811924001472}, + + author = {Tao Zhong and Xueyang Wu and others}, + fullauthor = {Tao Zhong and Xueyang Wu and Shujun Liang and Zhenyuan Ning and Li Wang and Yuyu Niu and Shihua Yang and Zhuang Kang and Qianjin Feng and Gang Li and Yu Zhang} + } diff --git a/paper_JOSS/paper.md b/paper_JOSS/paper.md new file mode 100644 index 000000000..728677222 --- /dev/null +++ b/paper_JOSS/paper.md @@ -0,0 +1,94 @@ +--- +title: 'Macapype: An open multi-software framework for non-human primate brain anatomical MRI processing ' +tags: + - Python + - Non human primate (NHP) + - anatomical MRI (Magentic Resonance Imaging) + - pipeline + - brain segmentation + - brain extraction + +license: "BSD-3-Clause" + +authors: + - name: David Meunier + orcid: 0000-0002-5812-6138 + affiliation: 1 + - name: Kep Kee Loh + orcid: 0000-0003-0650-224X + affiliation: 4 + - name: Bastien Cagna + orcid: 0009-0005-4243-5234 + affiliation: "2,3" + - name: Regis Trapeau + orcid: 0000-0003-1137-8669 + affiliation: 1 + - name: Julien Sein + orcid: 0000-0003-1767-5330 + affiliation: 1 + - name: Olivier Coulon + orcid: 0000-0003-4752-1228 + affiliation: 1 + +affiliations: + - name: Aix Marseille Univ, CNRS, INT, Inst Neurosci Timone, Marseille, France + index: 1 + - name: CATI, US52-UAR2031, CEA, ICM, Sorbonne Université, CNRS, INSERM, APHP, Ile de France, France + index: 2 + - name: Paris Brain Institute - Institut du Cerveau (ICM), Inserm U1127, CNRS UMR 7225, Sorbonne Universités UMR S 1127, ICM, F-75013 Paris, France. + index: 3 + - name: Department of Psychology, National University of Singapore, Singapore + index: 4 + + +date: 29 Oct 2025 +bibliography: paper.bib +--- + +## Summary + +Although brain anatomical Magnetic Resonance Imaging (MRI) processing is largely standardized and functional in humans, it remains a challenge to define robust processing pipelines for the segmentation of non-human primate (NHP) images. To unify the processing of NHP anatomical MRI, we propose Macapype, an open-source framework to create custom pipelines for data preparation, brain extraction, and brain segmentation. + +## Statement of Need +Non-human primates (NHPs) are increasingly used for neuroimaging studies due to the progress of MR acquisitions and the promises it holds in the field of neuroscience [@milham2018open]. Despite the standardization of MRI processing in humans with several well-known software options available, such as AFNI [@cox1996afni], FSL [@smith2004advances], SPM12 [@frackowiak1997human], and ANTS [@avants2011reproducible], defining robust processing pipelines for NHP anatomical image segmentation remains difficult. + +## Related Packages +Two categories of methods have been proposed to address the issue of NHP anatomical MR image segmentation. The first category corresponds to particular implementations for PNH images of existing human-MRI softwares such as **NHP-Freesurfer** and **CIVET-Macaque**, respectively relying on human-MRI softwares Freesurfer [@fischl2012freesurfer] and CIVET [@lepage2021civet]. The second category relies on the use of deep-learning and machine learning techniques, such as **U-Nets** , for example **nBEST** to provide brain mask, segmentation of GM, WM and subcurtical nuclei [@ZHONG2024120652]. The latter requires the use of GPUs, most existing softwares performs relatively badly on small NHP species such as marmoset due to the lack of flexibility in the processing steps and the variability of brain peculiarities among NHP species. + +## Presentation of the Package +In this context, we propose a general framework for the tissue segmentation of non-human primate brain MR images that can provide multiple pipelines to adapt to a variety of image qualities and species. This open-source framework, named Macapype, is built on the Nipype [@gorgolewski2011nipype], a widely used Python framework for human MRI analysis. + +The Macapype package was specifically designed to provide wraps of custom tools specific to NHP anatomical MRI preprocessingn, as well pipelines and workflows to achieve high-quality automated tissue segmentation of NHP anatomical images. In particular, the tuning of parameters for different species, should be possible if needed via the use parameters files + +![Different pipelines are chained\label{pipeline}](./pipelines2.png) + +## Pipelines + +Macapype provides configurable pipelines organized in three steps: data preparation, brain extraction, and brain segmentation. Post-processing allows for conversion to format for further processing outside Macapype. + +### Data Preparation Pipeline + +The data preparation pipeline is specified in a JSON parameters file and depends on individual parameters. If cropping parameters are absent, Macapype performs an automated but low-precision crop. The input volume is reoriented in a standard space, and denoising and debiasing steps are performed. + +### Brain Extraction Pipeline + +For skull-stripping step, Macapype offers a choice between AtlasBRex [@lohmeier2019atlasbrex] and bet4animal, an optimized version of brain extraction tool (BET in FSL) for NHP. HD-BET [@Isensee2019hdbet] is also available for deep-learning-based brain extraction. + +### Tissue Segmentation Pipeline + +Tissue segmentation is template-based and can be done in template or native space. Macapype provides templates for macaque, marmoset, baboon, and chimpanzee. T1xT2 debias is applied, followed by normalization and segmentation using ANTS-based Atropos or SPM12-based old segment. + +### Post-Processing Pipeline + +For compatibility with further processing, Macapype provides formatting options, such as the 5tt file from MRTrix [@tournier2019mrtrix3] for further processing of diffusion MRI and meshes in STL format for 3D printing. + +## Discussion + +Macapype is compatible with FAIR principles, storing all processing steps and parameters in a JSON file. It allows evaluation of results at different preprocessing steps and is tested on images from the PRIME-DE database [@milham2018open] and is listed as a software solution on PRIME-RE [@messinger2021collaborative]. + +## Acknowledgements + +We are grateful to Adrien Meguerditchian, Paul Apicella, and Guilhem Ibos for providing MRI datasets for testing. + +## References + diff --git a/paper_JOSS/paper.pdf b/paper_JOSS/paper.pdf new file mode 100644 index 000000000..6182aec69 Binary files /dev/null and b/paper_JOSS/paper.pdf differ diff --git a/paper_JOSS/pipelines.png b/paper_JOSS/pipelines.png new file mode 100644 index 000000000..a4d75cfb7 Binary files /dev/null and b/paper_JOSS/pipelines.png differ