diff --git a/paper_JOSS/brains.png b/paper_JOSS/brains.png deleted file mode 100644 index 5df8439a2..000000000 Binary files a/paper_JOSS/brains.png and /dev/null differ diff --git a/paper_JOSS/paper.bib b/paper_JOSS/paper.bib index 5e06a4500..0f0670398 100644 --- a/paper_JOSS/paper.bib +++ b/paper_JOSS/paper.bib @@ -1,5 +1,7 @@ @article{avants2011reproducible, title={ - A reproducible evaluation of ANTs similarity metric performance in brain image registration}, author={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} } + 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}} @@ -7,16 +9,21 @@ @article{balbastre2017primatologist @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 others}, journal={Nature methods}, volume={16}, number={1}, pages={111--116}, year={2019}, publisher={Nature Publishing Group} , doi={10.1038/s41592-018-0235-4} +@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 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{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={ @@ -28,23 +35,29 @@ @book{frackowiak1997human } @article{garcia2021preemacs, title={ - PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface}, author={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 others}, journal={Front. Neuroimform.}, volume={5}, pages={13}, year={2011}, publisher={Frontiers Media SA}, doi={10.3389/fninf.2011.00013} - } - -@article{gorgolewski2016brain, title={ - The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments}, author={Gorgolewski, K and others}, journal={Sci Data}, volume={3}, pages={160044}, year={2016}, publisher={Nature Publishing Group} , doi={10.1038/sdata.2016.44} - } - -@article{Isensee2019hdbet, title = { - Automated brain extraction of multisequence MRI using artificial neural networks}, author = {Isensee, Fabian and Schell, Marianne and Pflueger, Irada and Brugnara, Gianluca and Bonekamp, David and Neuberger, Ulf and Wick, Antje and Schlemmer, Heinz-Peter and Heiland, Sabine and Wick, Wolfgang and Bendszus, Martin and Maier-Hein, Klaus H. and Kickingereder, Philipp},journal = {Human Brain Mapping},volume = {40},number = {17},pages = {4952-4964},keywords = {artificial neural networks, brain extraction, deep learning, magnetic resonance imaging, neuroimaging, skull stripping},doi = {10.1002/hbm.24750},year = {2019} - } + 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. Neuroimform.}, volume={5}, pages={13}, year={2011}, publisher={Frontiers Media SA}, doi={10.3389/fninf.2011.00013} } + +@article{gorgolewski2016brain, title={The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments}, author={Gorgolewski, K and others}, journal={Sci Data}, volume={3}, pages={160044}, year={2016}, publisher={Nature Publishing Group} } + +@article{Isensee2019hdbet, +author = {Isensee, Fabian and Schell, Marianne and others}, +fullauthor = {Isensee, Fabian and Schell, Marianne and Pflueger, Irada and Brugnara, Gianluca and Bonekamp, David and Neuberger, Ulf and Wick, Antje and Schlemmer, Heinz-Peter and Heiland, Sabine and Wick, Wolfgang and Bendszus, Martin and Maier-Hein, Klaus H. and Kickingereder, Philipp}, +title = {Automated brain extraction of multisequence MRI using artificial neural networks}, +journal = {Human Brain Mapping}, +volume = {40}, +number = {17}, +pages = {4952-4964}, +keywords = {artificial neural networks, brain extraction, deep learning, magnetic resonance imaging, neuroimaging, skull stripping}, +doi = {10.1002/hbm.24750}, +year = {2019} +} @article{lepage2021civet, title={ - CIVET-Macaque: An automated pipeline for MRI-based cortical surface generation and cortical thickness in macaques}, author={Lepage, C and Wagstyl, K and Jung, B and Seidlitz, J and Sponheim, C and Ungerleider, L and Wang, X and Evans, AC and Messinger, A}, journal={Neuroimage}, volume={227}, pages={117622}, year={2021}, publisher={Elsevier}, doi={10.1016/j.neuroimage.2020.117622} + CIVET-Macaque: An automated pipeline for MRI-based cortical surface generation and cortical thickness in macaques}, author={Lepage, C and Wagstyl, K and others}, fullauthor={Lepage, C and Wagstyl, K and Jung, B and Seidlitz, J and Sponheim, C and Ungerleider, L and Wang, X and Evans, AC and Messinger, A}, journal={Neuroimage}, volume={227}, pages={117622}, year={2021}, publisher={Elsevier}, doi={10.1016/j.neuroimage.2020.117622} } @article{liu2021marmoset, title={ @@ -52,16 +65,17 @@ @article{liu2021marmoset } @article{lohmeier2019atlasbrex, title={ - AtlasBREX: Automated template-derived brain extraction in animal MRI}, author={Lohmeier, J and others}, journal={Sci Rep}, volume={9}, number={1}, pages={12219}, year={2019}, publisher={Nature Publishing Group}, doi={10.1038/s41598-019-48489-3} + AtlasBREX: Automated template-derived brain extraction in animal MRI}, author={Lohmeier, J and Kaneko, T and others}, + fullauthor = {Lohmeier, J, and Kaneko , T and Hamm , Bernd and Makowski , Marcus R. and Okano, Hideyuki}, journal={Sci Rep}, volume={9}, number={1}, pages={12219}, year={2019}, publisher={Nature Publishing Group}, doi={10.1038/s41598-019-48489-3} } @article{love2016average, title={The average baboon brain: MRI templates and tissue probability maps from 89 individuals}, author={Love, S and others}, journal={NeuroImage}, volume={132}, year={2016}, publisher={Elsevier}, doi={10.1016/j.neuroimage.2016.03.018} } @article{messinger2021collaborative, title={ - A Collaborative Resource Platform for Non-Human Primate Neuroimaging}, author={Messinger, A and others}, journal={NeuroImage}, volume={226}, pages={117519}, year={2021}, publisher={Elsevier}, doi ={10.1101/2020.07.31.230185} + A Collaborative Resource Platform for Non-Human Primate Neuroimaging}, author={Messinger, A and Sirmpilatze , Nikoloz and others}, journal={NeuroImage}, volume={226}, pages={117519}, year={2021}, publisher={Elsevier}, doi ={10.1101/2020.07.31.230185} } -@article{milham2018open, title={An Open Resource for Non-Human Primate Imaging}, author={Milham, MP and Ai, L and Koo, B and Xu, T and Amiez, C and Balezeau, F and Baxter, MG and Blezer, ELA and Brochier, T and Chen, A and others}, journal={Neuron}, volume={100}, pages={61--74}, year={2018}, publisher={Cell Press}, doi={10.1016/j.neuron.2018.08.039} } +@article{milham2018open, title={An Open Resource for Non-Human Primate Imaging}, author={Milham, MP and Ai, L and others}, fullauthor={Milham, MP and Ai, L and Koo, B and Xu, T and Amiez, C and Balezeau, F and Baxter, MG and Blezer, ELA and Brochier, T and Chen, A and others}, journal={Neuron}, volume={100}, pages={61--74}, year={2018}, publisher={Cell Press}, doi={10.1016/j.neuron.2018.08.039} } @article{routier2021clinica, title={ Clinica: an open source software platform for reproducible clinical neuroscience studies}, author={Routier, A and others}, journal={Frontiers in Neuroinformatics}, volume={15}, pages={689675}, year={2021}, publisher={Frontiers Media SA} ,doi={10.3389/fninf.2021.689675} @@ -72,7 +86,7 @@ @article{seidlitz2018population } @article{smith2004advances, title={ - Advances in functional and structural MR image analysis and implementation as FSL}, author={Smith, SM and Jenkinson, M and Woolrich, MW and Beckmann, CF and Behrens, TEJ and Johansen-Berg, H and Bannister, PR and De Luca, M and Drobnjak, I and Flitney, DE and Niazy, RK and Saunders, J and Vickers, J and Zhang, Y and De Stefano, N and Brady, JM and Matthews, PM}, journal={NeuroImage}, volume={23}, pages={208--219}, year={2004}, publisher={Elsevier}, doi={10.1016/j.neuroimage.2004.07.051} + Advances in functional and structural MR image analysis and implementation as FSL}, author={Smith, SM and Jenkinson, M and others},fullauthor={Smith, SM and Jenkinson, M and Woolrich, MW and Beckmann, CF and Behrens, TEJ and Johansen-Berg, H and Bannister, PR and De Luca, M and Drobnjak, I and Flitney, DE and Niazy, RK and Saunders, J and Vickers, J and Zhang, Y and De Stefano, N and Brady, JM and Matthews, PM}, journal={NeuroImage}, volume={23}, pages={208--219}, year={2004}, publisher={Elsevier}, doi={10.1016/j.neuroimage.2004.07.051} } @article{tasserie2019pypreclin, title={ @@ -80,7 +94,10 @@ @article{tasserie2019pypreclin } @article{tournier2019mrtrix3, title={ - MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation}, author={Tournier, J-D and Smith, R and Raffelt, D and Tabbara, R and Dhollander, T and Pietsch, M and Christiaens, D and Jeurissen, B and Yeh, C-H and Connelly, A}, journal={NeuroImage}, volume={202}, pages={116--137}, year={2019}, publisher={Elsevier}, doi={10.1016/j.neuroimage.2019.116137} + MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation}, + author={Tournier, J-D and Smith, R and others}, + fullauthor={Tournier, J-D and Smith, R and Raffelt, D and Tabbara, R and Dhollander, T and Pietsch, M and Christiaens, D and Jeurissen, B and Yeh, C-H and Connelly, A}, + journal={NeuroImage}, volume={202}, pages={116--137}, year={2019}, publisher={Elsevier}, doi={10.1016/j.neuroimage.2019.116137} } @article{vickery2020chimpanzee, title={ @@ -111,5 +128,8 @@ @article{ZHONG2021117649 } @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 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} + 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 index db744dfd1..728677222 100644 --- a/paper_JOSS/paper.md +++ b/paper_JOSS/paper.md @@ -41,90 +41,54 @@ affiliations: index: 4 -date: 25 Jun 2025 +date: 29 Oct 2025 bibliography: paper.bib --- -# Macapype: An Open Multi-Software Framework for Non-Human Primate Brain Anatomical MRI Processing - ## 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]. Structural MR images are essential for accessing anatomical information, defining regions of interest for functional and diffusion studies, providing cortical surface reconstruction, or localizing implanted electrodes for electrophysiology. 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. Standard human pipelines do not work directly on NHP images due to differences in head geometry, size, and anatomical content (e.g., large muscles). Moreover, acquisition parameters and experimental settings are much more variable in NHP studies than in human studies, making it challenging to define a single method to segment properly anatomical MR images of all NHP species. Therefore, there is a real need for efficient, versatile software that can handle the variability of encountered images. +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 - -Several methods have been proposed to address the issue of NHP anatomical MR image segmentation. Some of these have been built as adaptations of existing methods originally developed for human images. For instance: - -- **NHP-Freesurfer**: An adaptation of Freesurfer [@fischl2012freesurfer] that uses the NMT macaque atlas to segment macaque images and extract surfaces. -- **CIVET-Macaque**: An adaptation of the CIVET method [@lepage2021civet] to extract cortical surfaces from macaque MR images. -- **PREEMACS**: Uses various functions from FSL , ANTS , MRTrix [@tournier2019mrtrix3], MRIqc [@esteban2017mriqc], and Freesurfer [@fischl2012freesurfer] to extract cortical surfaces from MR images and register them to the same template. -- **Precon-all**: Uses a combination of ANTS, FSL, and Freesurfer to segment images and extract surfaces. A major drawback is the dependencies on five user-defined masks, including a brain mask, left and right hemisphere masks, a non-cortical mask (cerebellum and brain stem), and a subcortical (medial wall) mask. -- **U-Nets**: Used to perform brain extraction from macaque MR images [@ZHONG2021117649]. -- **nBEST**: DeepLearning program used to provide brain mask, segmentation of GM, WM and subcurtical nuclei [@ZHONG2024120652]. *Requires the use of GPUs, and performs relatively badly on small PNH species such as marmoset*. - -Unfortunately, none of these software solutions are versatile and flexible enough to fully perform segmentation while handling the variety of species and image characteristics encountered in NHP neuroimaging studies. +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. -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 framework [@gorgolewski2011nipype]. Nipype is a widely used Python framework for human MRI analysis, providing tools for building pipelines for diffusion, structural, and functional MRI. It provides "wraps" of different software, such as AFNI, FSL, SPM12, and ANTS, to build pipelines that mix functions requiring different scripting languages in a unified framework. - -The Macapype package was specifically designed to provide: - -1. Wraps of custom tools specific to NHP anatomical MRI preprocessing, such as AtlasBRex [@lohmeier2019atlasbrex] and NMT-based alignment [@seidlitz2018population]. -2. Pipelines and workflows specific to different NHP species and MRI acquisition sequences to achieve high-quality automated tissue segmentation of NHP anatomical images. In particular, the tuning of parameters for different species, and even more specifically of different individuals of the same species, should be possible if needed via the use parameters files +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 several pipelines that may be configured depending on processing needs, and are organized in 3 steps: data preparation, brain extraction, and brain segmentation pipelines. An extra postprocessing pipeline is also available for data formatting for external use (see Figure \ref{pipeline}) - -![Different pipelines are chained\label{pipeline}](./pipelines.png) +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 composition and order of the steps in the data preparation pipeline are specified in a json paramaters file and will also depend on the individual parameters provided. One of the most important manual steps is setting the cropping parameters for the image, requiring a manual inspection of all subject/session images. If the cropping parameters are absent, Macapype will result in an automated but low-precision crop. If cropping parameters are provided for T1w only and a T2w image is also available, the T2w image will be aligned to the T1w image, and the cropping parameters will be used for both images. If cropping parameters are provided for both T1w and T2w, the crop will be performed independently, and only then will the cropped T2w be aligned to the cropped T1w. It is even possible to crop independently each T1w and T2w separated acquisitions, although the standard is to average all the images of a given type. - -Note that input volume needs to be reoriented in standard space (e.g., using fslreorient2std if NIFTI orientation is correctly labeled), so that AC-PC line of brain is parallel to Y-axis. - - -Finally, a step of denoising using non-local means methods can be optionally performed before a mandatory debiasing step using N4debias (ANTS). +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 the skull-stripping step, Macapype provides a choice between AtlasBRex [@lohmeier2019atlasbrex] and bet4animal, an optimized version of BET (FSL) specifically for NHP. It is also possible to use hd-bet [@Isensee2019hdbet] relying on deep-learning brain extraction. +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 pipelines are only based on template-based segmentation in Macapype, but segmentation can be done either in template or native space. In both cases, normalization between scanner-based and T1 template is required, as priors are required to be projected in native space if segmentation in native space is used. We provide at least one template for the following NHP species: macaque (inia19 and NMT 1.3), marmoset (MBM v3.0.1), baboon (haiko89), and chimpanzee (JunaChimp). - -We apply T1xT2 (T1wxT2w) debias on brain-only T1w and T2w images as a first step of brain segmentation. Then, normalization to template space can be done either using NMT provided tools (even if the NMT template is not used) or an in-house iterative registration tool. Finally, the tissue segmentation processing is performed with the ANTS-based Atropos method or the SPM-based old segment method. +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 -Two formatting options are provided: - -1. Reorganizing the different tissues into one single file in the order expected by the 5tt file from MRTrix. -2. Generating meshes using IsoSurface (AFNI), in particular the white matter + gray matter (wm+gm), as well as each tissue mesh independently. - -## Examples - -We provide illustrations obtained by applying the standard pipelines to three different species (macaque, baboon, and marmoset). - -![Different species at different steps of the processing \label{example}](./brains.png) - +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, with the creation and storage of a 'final' json parameter file containing all the arguments, version and the processing steps that were used for any given instance of processing. We also provide a MRI sample for 3 different species (macaque, marmoset and baboon), corresponding to the data used in Figure \ref{example}. We believe it is crucial for the community to share segmentations for benchmarking. It is of particular importance that Deep learning based segmentation (e.g. [@Wang2021unet] [@ZHONG2024120652]) are evaluated on benchmark datasets and compared to ground truth. - -Macapype provides a unified framework to perform image processing with the ability to evaluate the results obtained at different preprocessing steps compared to ground truth and to assess compatibility between the steps. This allows for choosing the most adapted steps for a given custom analysis, depending on the availability and quality of the data present in the dataset (e.g., if T1w or T1w and T2w images are available, the quality of the acquisition due to antenna constraints). For easier dissemination, Macapype has been tested on various images from the PRIME-DE database [@milham2018open] and is listed as a software solution on PRIME-RE [@messinger2021collaborative]. +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 using sample of their MRI datasets as exemples and tests on the different species. +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 index 0e64495fc..6182aec69 100644 Binary files a/paper_JOSS/paper.pdf and b/paper_JOSS/paper.pdf differ diff --git a/paper_JOSS/pipelines.png b/paper_JOSS/pipelines.png index 652444b7d..a4d75cfb7 100644 Binary files a/paper_JOSS/pipelines.png and b/paper_JOSS/pipelines.png differ