Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer
Robust medical image segmentation across imaging modalities is challenging because of large differences in appearance and intensity distributions. Models trained on a single modality often show substantial performance drops when applied to unseen domains. In this work, we develop a unified 3D pancreas segmentation framework that applies domain-adversarial learning to 4,604 heterogeneous CT and MRI scans to learn anatomical representations. A shared nnU-Net encoder-decoder is trained for whole-pancreas segmentation, with a latent domain discriminator encouraging CT-MRI feature alignment. The le
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- PossiblePossibly related (embedding) · 54%Dual-phase deep learning models improve pancreatic cyst risk assessment - News-Medical →
- LinkedLinked via arxiv author · 85%Ziliang Hong →
“Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer”
- LinkedLinked via arxiv author · 85%Hongyi Pan →
“Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer”
- LinkedLinked via arxiv author · 85%Halil Ertugrul Aktas →
“Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer”
- LinkedLinked via arxiv author · 85%Andrea Bejar →
“Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer”
- LinkedLinked via arxiv author · 85%Elif Keles →
“Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer”
- LinkedLinked via arxiv author · 85%Frank H. Miller →
“Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer”
- LinkedLinked via arxiv author · 85%Michael B. Wallace →
“Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer”