An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis.
Authors
Affiliations (9)
Affiliations (9)
- University of California Irvine, Department of Radiological Sciences, Irvine, CA, 92617, USA.
- Boston Children's Hospital and Harvard Medical School, Department of Radiology, Boston, MA, 02115, USA.
- Boston Children's Hospital and Harvard Medical School, Department of Neurology, Boston, MA, 02115, USA.
- Northeastern University, Department of Electrical and Computer Engineering, Boston, MA, 02115, USA.
- ADIA Lab., Abu Dhabi, United Arab Emirates.
- Massachusetts General Hospital and Harvard Medical School, Department of Radiology, Boston, MA, 02114, USA.
- Boston Children's Hospital and Harvard Medical School, Department of Pediatrics, Boston, MA, 02115, USA.
- Boston Children's Hospital and Harvard Medical School, Department of Neurology, Boston, MA, 02115, USA. [email protected].
- University of California Irvine, Department of Radiological Sciences, Irvine, CA, 92617, USA. [email protected].
Abstract
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.