Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer

2026-09-11

AI Summary

本文通过域对抗学习方法解决跨模态胰腺分割问题，使用统一的3D框架处理CT和MRI图像，实现高效标注转移。

Abstract

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 MRI-only subregion annotations. An average Dice score of 87.31% on the in-distribution test set and Dice scores ranging from 84.20% to 88.09% across external OOD datasets were achieved in whole pancreas segmentation. Dice scores of 80.53% on MRI and 83.05% on CT were achieved for downstream subregion segmentation, without using CT subregion annotations. These results demonstrate that a unified anatomical representation can support both cross-modality pancreas segmentation and label-efficient downstream transfer.

Problem

• medical image segmentation
• cross-modality
• appearance and intensity distributions
• performance drops
• unseen domains

Innovation

• domain-adversarial learning
• cross-modality segmentation
• label-efficient transfer
• unified 3D pancreas segmentation