Li Jiapeng, Zhang Yifan, Xu Lisheng, Yao Yudong, Qi Lin
IEEE J Biomed Health Inform. 2025 Jun;29(6):3874-3885. doi: 10.1109/JBHI.2024.3507092.
The differences between cross-modality medical images are significant, so several studies are working on unsupervised domain adaptation (UDA) segmentation, which aims to adapt a segmentation model trained on a labeled source domain to an unlabeled target domain. The conventional UDA segmentation strategy aims to integrate image generation and segmentation. However, conventional image generation modules only consider information from a single domain (source or target), resulting in visual inconsistencies. The image generation module may also lack anatomical constraints, leading to incorrect pseudo-label generation. To address these issues, we propose an improved symmetric generative adversarial network (ISGAN). Unlike conventional approaches that perform domain adaptation only in the source or target domain, ISGAN adopts a symmetric architecture using two-path domain adaptation to reduce the visual difference. In addition, ISGAN adopts a bidirectional training strategy to optimize the image generation and segmentation modules. The bidirectional training strategy introduces the anatomical constraints into the image generation module, thereby reducing the generation of incorrect pseudo labels. Finally, we validate ISGAN on two cross-modality datasets (the MMWHS cardiac dataset and Abdomen dataset). ISGAN delivers promising segmentation and generalization performance compared with state-of-the-art UDA methods.
跨模态医学图像之间的差异很大，因此有几项研究致力于无监督域适应（UDA）分割，其目的是将在有标签的源域上训练的分割模型应用于无标签的目标域。传统的UDA分割策略旨在整合图像生成和分割。然而，传统的图像生成模块只考虑来自单个域（源域或目标域）的信息，导致视觉上的不一致。图像生成模块也可能缺乏解剖学约束，从而导致错误的伪标签生成。为了解决这些问题，我们提出了一种改进的对称生成对抗网络（ISGAN）。与仅在源域或目标域中执行域适应的传统方法不同，ISGAN采用对称架构，使用双路径域适应来减少视觉差异。此外，ISGAN采用双向训练策略来优化图像生成和分割模块。双向训练策略将解剖学约束引入图像生成模块，从而减少错误伪标签的生成。最后，我们在两个跨模态数据集（MMWHS心脏数据集和腹部数据集）上验证了ISGAN。与最先进的UDA方法相比，ISGAN具有良好的分割和泛化性能。
