对比对抗混合学习用于无监督跨模态图像分割

文献信息

中文摘要

背景： 无监督域适应（UDA）因其能够解决有标签源域图像与无标签目标域图像之间分布偏移导致的性能下降而受到广泛研究。在放射治疗中，这一问题尤其与将分割知识从计划计算机断层扫描（CT）迁移至锥形束计算机断层扫描（CBCT）相关，因为目标域标注有限。本研究旨在开发一种对比对抗混合学习框架，用于无监督跨模态图像分割。
方法： 开发了一种整合混合增强（MA）与对比对抗学习（AL）的UDA框架。该框架包括一个共享权重重分配网络以减少跨域外观差异，一种混合一致性策略以约束混合域图像的分割预测，以及对比AL以调节重分配混合图像与混合重分配图像之间的关系。该方法在两个内部CT-CBCT数据集上进行了评估，这些数据集包含112例鼻咽癌患者和112例乳腺癌患者，每个数据集随机选取12例患者用于测试。在公共脑肿瘤分割2018（BraTS2018）数据集的75例患者中进一步评估了泛化性，其中80%用于训练，20%用于测试。分割性能通过Dice相似系数（DSC）、Hausdorff距离（HD）和Jaccard指数进行评估。
结果： 在内部CT-CBCT数据集上，完整的UDA + MA + AL框架对乳腺癌临床靶区（CTV）、心脏、头颈癌CTV和腮腺的DSC值分别达到82.08%、85.81%、78.60%和60.75%；同时，与仅UDA框架相比，完整框架相应的绝对DSC提升分别为8.70、1.02、3.28和3.04个百分点。在BraTS2018数据集上，所提出的框架在将T2加权磁共振图像（T2）适应至T1加权磁共振图像（T1）、T2适应至T1对比增强、T2适应至液体衰减反转恢复（FLAIR）以及FLAIR适应至T2的任务中，DSC值分别达到63.56%、67.81%、85.47%和74.16%。总体而言，与代表性UDA分割方法相比，所提出的方法平均DSC提升3.53个百分点。
结论： 所提出的对比对抗混合学习框架在CT-CBCT和多序列磁共振成像适应任务中提高了无监督跨模态分割性能。通过结合跨域重分配、混合一致性学习和对比AL，该框架能够在保持分割一致性的同时，为图像外观和解剖结构的变化提供增强的鲁棒性。

英文摘要

BACKGROUND: Unsupervised domain adaptation (UDA) has been extensively investigated in terms of its ability to address the performance degradation caused by distribution shifts between labeled source-domain images and unlabeled target-domain images. In radiotherapy, this problem is particularly relevant for the transfer of segmentation knowledge from planning computed tomography (CT) to cone-beam computed tomography (CBCT), for which target-domain annotations are limited. This study aimed to develop a contrastive adversarial mix-up learning framework for unsupervised cross-modality image segmentation.
METHODS: A UDA framework integrating mix-up augmentation (MA) and contrastive adversarial learning (AL) was developed. This framework included a shared-weight redistribution network to reduce cross-domain appearance discrepancies, a mix-up consistency strategy to constrain segmentation predictions of mixed-domain images, and contrastive AL to regulate the relationship between redistributed mixture images and mixed redistributed images. The method was evaluated with two in-house CT-CBCT datasets comprising 112 patients with nasopharyngeal carcinoma and 112 patients with breast cancer, with 12 patients from each dataset randomly selected for testing. Generalizability was further evaluated in 75 patients from the public Brain Tumor Segmentation 2018 (BraTS2018) dataset, with 80% used for training and 20% for testing. Segmentation performance was assessed via the Dice similarity coefficient (DSC), Hausdorff distance (HD), and Jaccard index.
RESULTS: On the in-house CT-CBCT datasets, the complete UDA + MA + AL framework achieved DSC values of 82.08%, 85.81%, 78.60%, and 60.75% for the breast cancer clinical target volume (CTV), heart, head-and-neck cancer CTV, and parotid glands, respectively; meanwhile, compared with the UDA-only framework, the corresponding absolute DSC improvements of the complete framework were 8.70, 1.02, 3.28, and 3.04 percentage points, respectively. On the BraTS2018 dataset, the proposed framework achieved DSC values of 63.56%, 67.81%, 85.47%, and 74.16% for tasks adapting T2-weighted magnetic resonance image (T2) to T1-weighted magnetic resonance image (T1), T2 to T1 contrast enhanced, T2 to fluid-attenuated inversion recovery (FLAIR), and FLAIR to T2, respectively. Overall, the proposed method achieved an average DSC improvement of 3.53 percentage points compared with representative UDA-based segmentation methods.
CONCLUSIONS: The proposed contrastive adversarial mix-up learning framework improved unsupervised cross-modality segmentation performance across CT-CBCT and multisequence magnetic resonance imaging adaptation tasks. By combining cross-domain redistribution, mix-up consistency learning, and contrastive AL, the framework can provide enhanced robustness for variations in image appearance and anatomical structure while maintaining segmentation consistency.