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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.15034 |
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| _version_ | 1866916005780914176 |
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| author | Bagheri, Mahdi Velasco-Annis, Clemente Wang, Jian Faghihpirayesh, Razieh Khan, Shadab Calixto, Camilo Jaimes, Camilo Vasung, Lana Ouaalam, Abdelhakim Afacan, Onur Warfield, Simon K. Rollins, Caitlin K. Gholipour, Ali |
| author_facet | Bagheri, Mahdi Velasco-Annis, Clemente Wang, Jian Faghihpirayesh, Razieh Khan, Shadab Calixto, Camilo Jaimes, Camilo Vasung, Lana Ouaalam, Abdelhakim Afacan, Onur Warfield, Simon K. Rollins, Caitlin K. Gholipour, Ali |
| contents | Characterizing in-utero brain development is essential for understanding typical and atypical neurodevelopment. Building on prior spatiotemporal fetal brain MRI atlases, we present the CRL-2025 fetal brain atlas, a spatiotemporal (4D) atlas of the developing fetal brain between 21 and 37 gestational weeks. This atlas is constructed from MRI scans of 159 fetuses with typically developing brains using a diffeomorphic deformable registration framework integrated with kernel regression on age. CRL-2025 uniquely includes detailed tissue segmentations, transient white matter compartments, and parcellation into 126 anatomical regions. It offers significantly enhanced anatomical details over the CRL-2017 atlas and is presented along with a re-release of the CRL diffusion MRI atlas featuring newly created tissue segmentation and labels. We release de-identified, processed subject-level fetal MRI datasets used to generate CRL-2025, providing input-output transparency and reproducibility. We also provide FetalSEG, a deep learning-based multiclass segmentation tool to facilitate automatic fetal brain MRI segmentation. The CRL-2025 atlas and its tools enable scalable fetal brain MRI segmentation, analysis, and neurodevelopmental research for the broader community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15034 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis Bagheri, Mahdi Velasco-Annis, Clemente Wang, Jian Faghihpirayesh, Razieh Khan, Shadab Calixto, Camilo Jaimes, Camilo Vasung, Lana Ouaalam, Abdelhakim Afacan, Onur Warfield, Simon K. Rollins, Caitlin K. Gholipour, Ali Quantitative Methods Characterizing in-utero brain development is essential for understanding typical and atypical neurodevelopment. Building on prior spatiotemporal fetal brain MRI atlases, we present the CRL-2025 fetal brain atlas, a spatiotemporal (4D) atlas of the developing fetal brain between 21 and 37 gestational weeks. This atlas is constructed from MRI scans of 159 fetuses with typically developing brains using a diffeomorphic deformable registration framework integrated with kernel regression on age. CRL-2025 uniquely includes detailed tissue segmentations, transient white matter compartments, and parcellation into 126 anatomical regions. It offers significantly enhanced anatomical details over the CRL-2017 atlas and is presented along with a re-release of the CRL diffusion MRI atlas featuring newly created tissue segmentation and labels. We release de-identified, processed subject-level fetal MRI datasets used to generate CRL-2025, providing input-output transparency and reproducibility. We also provide FetalSEG, a deep learning-based multiclass segmentation tool to facilitate automatic fetal brain MRI segmentation. The CRL-2025 atlas and its tools enable scalable fetal brain MRI segmentation, analysis, and neurodevelopmental research for the broader community. |
| title | An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2508.15034 |