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 learned encoder is subsequently transferred to pancreatic head-body-tail segmentation using limited...
| Authors | Ziliang Hong, Hongyi Pan, Halil Ertugrul Aktas et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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