Keywords: Analysis/Processing, Segmentation, Fetal Brain MRI; Artificial Intelligence
Motivation: Automatic segmentation of fetal brain remains challenging partially due to the dynamically changing anatomical structures during fetal brain development.
Goal(s): To enhance segmentation accuracy through incorporating gestational age-specific information as a guidance, we introduce AtlasSeg, a dual-U-shape network with dense attentive interactions.
Approach: By providing atlas volume and segmentation label at the corresponding gestational age, AtlasSeg effectively extracts the contextual features of age-specific patterns and structures that assist segmentations.
Results: AtlasSeg demonstrated superior performance against six other segmentation networks in both standard and out-of-distribution experiments, in two fetal MRI datasets. Ablation tests further demonstrated the role of atlas guidance.
Impact: Through gestational age-specific atlas-guided information, AtlasSeg can serve as an accurate and robust automatic segmentation tool for its superior performance in both in-distribution and out-of-distribution tests, which is useful for quantitative analysis in large-scale fetal brain studies.
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