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

• URL: http://arxiv.org/abs/2609.13043v1
• Date: Fri, 11 Sep 2026 16:40:25 GMT
• Title: Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer
• Abstract summary: We develop a unified 3D pancreas segmentation framework that applies domain-adversarial learning to heterogeneous CT and MRI scans.<n>A shared nnU-Net encoder-decoder is trained for whole-pancreas segmentation, with a latent domain discriminator encouraging CT-MRI feature alignment.<n>A unified anatomical representation can support both cross-modality pancreas segmentation and label-efficient downstream transfer.
• Score: 9.697026577787588
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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