统一CT和MRI胰腺分割用于标签高效跨模态亚区迁移
Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer
- Northwestern University（西北大学）
- Mayo Clinic Florida（梅奥诊所佛罗里达分院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出统一3D胰腺分割框架，利用域对抗学习对齐CT和MRI特征，实现跨模态分割及标签高效亚区迁移，全胰腺Dice达87.31%，亚区分割在MRI和CT上分别达80.53%和83.05%。
AI中文摘要：
跨成像模态的鲁棒医学图像分割因外观和强度分布的显著差异而具有挑战性。在单一模态上训练的模型应用于未见领域时，性能往往大幅下降。在本工作中，我们开发了一个统一的3D胰腺分割框架，将域对抗学习应用于4,604个异质CT和MRI扫描，以学习解剖表示。一个共享的nnU-Net编码器-解码器被训练用于全胰腺分割，其中潜在域判别器促进CT-MRI特征对齐。学习到的编码器随后被迁移到使用有限MRI-only亚区标注的胰头-体-尾分割。在全胰腺分割中，在分布内测试集上取得了87.31%的平均Dice分数，在外部OOD数据集上Dice分数范围从84.20%到88.09%。在下游亚区分割中，在MRI上取得了80.53%的Dice分数，在CT上取得了83.05%的Dice分数，且未使用CT亚区标注。这些结果表明，统一的解剖表示可以支持跨模态胰腺分割和标签高效的下游迁移。
英文摘要：
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.
