Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation

2025-08-06

AI Summary

Fetal brain MRI assessment is hindered by developmental heterogeneity, inter-site scanning protocol variations, and imprecise gestational age estimation, necessitating age-specific standardized reference atlases. To address this, we propose a conditional generative deep learning framework that integrates differentiable image registration with a conditional adversarial discriminator, enabling end-to-end mapping from gestational age to dynamic fetal brain anatomy and supporting real-time generation of continuous, age-specific brain atlases. Trained and validated on 219 normal fetal T2-weighted MRI scans, the model achieves a mean Dice score of 86.3% across six brain tissue classes, accurately recapitulating neurodevelopmental trajectories. The resulting atlas demonstrates strong robustness, cross-center generalizability, and clinically feasible inference speed (<1 second per scan). This work establishes the first learnable, updatable, and standardized reference for individualized fetal brain development assessment.

Technology Category

• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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

Problem

• Develops continuous age-specific fetal brain atlases for segmentation
• Addresses variability in fetal brain maturation and imaging protocols
• Enables real-time individualized fetal brain developmental assessment

Innovation

• Deep-learning framework for age-specific fetal brain atlases
• Combines direct registration with conditional discriminator
• Achieves high accuracy and real-time segmentation performance