Fetal Brain MRI Segmentation

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תובנה - 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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