The presence of domain shift in medical imaging is a common issue, which can greatly impact the performance of segmentation models when dealing with unseen image domains. Adversarial-based deep learning models, such as Cycle-GAN, have become a common model for approaching unsupervised domain adaptation of medical images. These models however, have no ability to enforce the preservation of structures of interest when translating medical scans, which can lead to potentially poor results for unsupervised domain adaptation within the context of segmentation. This work introduces the Structure Preserving Cycle-GAN (SP Cycle-GAN), which promotes medical structure preservation during image translation through the enforcement of a segmentation loss term in the overall Cycle-GAN training process. We demonstrate the structure preserving capability of the SP Cycle-GAN both visually and through comparison of Dice score segmentation performance for the unsupervised domain adaptation models. The SP Cycle-GAN is able to outperform baseline approaches and standard Cycle-GAN domain adaptation for binary blood vessel segmentation in the STARE and DRIVE datasets, and multi-class Left Ventricle and Myocardium segmentation in the multi-modal MM-WHS dataset. SP Cycle-GAN achieved a state of the art Myocardium segmentation Dice score (DSC) of 0.7435 for the MR to CT MM-WHS domain adaptation problem, and excelled in nearly all categories for the MM-WHS dataset. SP Cycle-GAN also demonstrated a strong ability to preserve blood vessel structure in the DRIVE to STARE domain adaptation problem, achieving a 4% DSC increase over a default Cycle-GAN implementation.

翻译：医学影像中领域偏移的存在是常见问题，这会严重影响分割模型处理未见图像领域时的性能。基于对抗的深度学习模型（如Cycle-GAN）已成为解决医学图像无监督领域自适应的常用模型。然而，这些模型在转换医学扫描图像时无法确保感兴趣结构的保持，这可能导致无监督领域自适应在分割场景下产生较差结果。本文提出了结构保持循环生成对抗网络（SP Cycle-GAN），通过在Cycle-GAN整体训练过程中引入分割损失项，促进图像转换过程中的医学结构保持。我们通过可视化比较以及无监督领域自适应模型的Dice系数分割性能对比，验证了SP Cycle-GAN的结构保持能力。在STARE和DRIVE数据集上的二值血管分割任务中，SP Cycle-GAN优于基准方法和标准Cycle-GAN领域自适应；在多模态MM-WHS数据集上的左心室和心肌多类分割任务中同样表现卓越。针对MR到CT的MM-WHS领域自适应问题，SP Cycle-GAN实现了0.7435的心肌分割Dice系数（DSC）最新成果，并在MM-WHS数据集的几乎所有类别中表现优异。在DRIVE到STARE的领域自适应问题中，SP Cycle-GAN展现出强大的血管结构保持能力，相比默认Cycle-GAN实现取得了4%的Dice系数提升。