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インサイト - Fetal Brain MRI Segmentation
Improving Fetal Brain MRI Segmentation with Synthetic Data: Exploring Intensity Clustering and Real Image Fine-tuning for Enhanced Domain Generalization
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.
AtlasSeg: Enhancing Fetal Brain MRI Segmentation Using Atlas Priors and Dual-U-Net Architecture with Multi-Scale Attentive Fusion
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.
Improving Cross-Domain Fetal Brain MRI Segmentation with Synthetic Data
Synthetic data improves fetal brain MRI segmentation across diverse datasets.
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