
Synthetic data generation, particularly using intensity clustering and real image fine-tuning, significantly improves the domain generalization of deep learning models for fetal brain MRI segmentation, outperforming traditional physics-based simulations and achieving comparable results to state-of-the-art methods trained on larger, multi-domain datasets.


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improving-fetal-brain-mri-segmentation-with-synthetic-data-exploring-intensity-clustering-and-real-image-fine-tuning-for-enhanced-domain-generalization


Improving Fetal Brain MRI Segmentation with Synthetic Data: Exploring Intensity Clustering and Real Image Fine-tuning for Enhanced Domain Generalization


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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.


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



Synthetic data improves fetal brain MRI segmentation across diverse datasets.


improving-cross-domain-fetal-brain-mri-segmentation-with-synthetic-data


Improving Cross-Domain Fetal Brain MRI Segmentation with Synthetic Data
