Domain shift has been a long-standing issue for medical image segmentation. Recently, unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentation performance by distilling knowledge from a label-rich source domain to a target domain without labels. In this work, we propose a multi-scale self-ensembling based UDA framework for automatic segmentation of two key brain structures i.e., Vestibular Schwannoma (VS) and Cochlea on high-resolution T2 images. First, a segmentation-enhanced contrastive unpaired image translation module is designed for image-level domain adaptation from source T1 to target T2. Next, multi-scale deep supervision and consistency regularization are introduced to a mean teacher network for self-ensemble learning to further close the domain gap. Furthermore, self-training and intensity augmentation techniques are utilized to mitigate label scarcity and boost cross-modality segmentation performance. Our method demonstrates promising segmentation performance with a mean Dice score of 83.8% and 81.4% and an average asymmetric surface distance (ASSD) of 0.55 mm and 0.26 mm for the VS and Cochlea, respectively in the validation phase of the crossMoDA 2022 challenge.

翻译：域偏移一直是医学图像分割领域的长期挑战。近期，无监督域适应（UDA）方法通过从标注丰富的源域向无标签目标域进行知识蒸馏，在跨模态分割任务中取得了显著进展。本文提出一种基于多尺度自集成UDA框架，用于高分辨率T2图像中两个关键脑结构（即前庭神经鞘瘤（VS）和耳蜗）的自动分割。首先，设计了一种分割增强型对比无配对图像翻译模块，实现从源域T1到目标域T2的图像级域适应。其次，在多尺度深度监督与一致性正则化基础上，引入均值教师网络进行自集成学习，进一步缩小域差距。此外，利用自训练与强度增强技术缓解标签稀缺问题，提升跨模态分割性能。本方法在crossMoDA 2022挑战赛验证阶段取得了优异的分割效果：VS与耳蜗的平均Dice分数分别为83.8%和81.4%，平均不对称表面距离（ASSD）分别为0.55毫米和0.26毫米。