Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation

Magnetic Resonance Imaging (MRI) of the fetal brain has become a key tool for studying brain development in vivo. Yet, its assessment remains challenging due to variability in brain maturation, imaging protocols, and uncertain estimates of Gestational Age (GA). To overcome these, brain atlases provide a standardized reference framework that facilitates objective evaluation and comparison across subjects by aligning the atlas and subjects in a common coordinate system. In this work, we introduce a novel deep-learning framework for generating continuous, age-specific fetal brain atlases for real-time fetal brain tissue segmentation. The framework combines a direct registration model with a conditional discriminator. Trained on a curated dataset of 219 neurotypical fetal MRIs spanning from 21 to 37 weeks of gestation. The method achieves high registration accuracy, captures dynamic anatomical changes with sharp structural detail, and robust segmentation performance with an average Dice Similarity Coefficient (DSC) of 86.3% across six brain tissues. Furthermore, volumetric analysis of the generated atlases reveals detailed neurotypical growth trajectories, providing valuable insights into the maturation of the fetal brain. This approach enables individualized developmental assessment with minimal pre-processing and real-time performance, supporting both research and clinical applications. The model code is available at https://github.com/cirmuw/fetal-brain-atlas

A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and for images acquired at different centers than those used for training, with labeling errors that violate expert knowledge about the anatomy and the intensity distribution of the regions to be segmented. Such errors undermine the trustworthiness of deep learning models developed for medical image segmentation. Mechanisms with a fallback method for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). Here, we propose a principled trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that are likely to violate expert knowledge and relies on a fallback atlas-based segmentation method for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal T2w MRI consisting of 540 manually annotated fetal brain 3D MRIs with neurotypical or abnormal brain development and acquired from 13 sources of data across 6 countries. We show that our trustworthy AI method improves the robustness of a state-of-the-art backbone AI for fetal brain MRI segmentation on MRIs acquired across various centers and for fetuses with various brain abnormalities.