
AtlasSeg, a novel deep learning model, leverages gestational age-specific atlas priors and a dual-U-Net architecture with multi-scale attentive fusion to significantly improve the accuracy of cortical segmentation in fetal brain MRI, outperforming existing state-of-the-art methods.


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atlasseg-enhancing-fetal-brain-mri-segmentation-using-atlas-priors-and-dual-u-net-architecture-with-multi-scale-attentive-fusion


AtlasSeg: Enhancing Fetal Brain MRI Segmentation Using Atlas Priors and Dual-U-Net Architecture with Multi-Scale Attentive Fusion


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Synthetic data improves fetal brain MRI segmentation across diverse datasets.


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Improving Cross-Domain Fetal Brain MRI Segmentation with Synthetic Data
