Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance when in fully supervised condition. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation can alleviate this problem, which makes it possible to use annotated data in one imaging modality to train a network that can successfully perform segmentation on target imaging modality with no labels. In this work, we propose a self-training based unsupervised domain adaptation framework for 3D medical image segmentation named COSMOS and validate it with automatic segmentation of Vestibular Schwannoma (VS) and cochlea on high-resolution T2 Magnetic Resonance Images (MRI). Our target-aware contrast conversion network translates source domain annotated T1 MRI to pseudo T2 MRI to enable segmentation training on target domain, while preserving important anatomical features of interest in the converted images. Iterative self-training is followed to incorporate unlabeled data to training and incrementally improve the quality of pseudo-labels, thereby leading to improved performance of segmentation. COSMOS won the 1\textsuperscript{st} place in the Cross-Modality Domain Adaptation (crossMoDA) challenge held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). It achieves mean Dice score and Average Symmetric Surface Distance of 0.871(0.063) and 0.437(0.270) for VS, and 0.842(0.020) and 0.152(0.030) for cochlea.

翻译：基于深度学习的最新医学图像分割研究在全监督条件下已接近人类水平。然而，在医学影像领域获取像素级专家标注极其昂贵且耗时。无监督域适应可缓解此问题，使得利用一种成像模态的标注数据训练网络、进而在无标签的目标成像模态上成功执行分割成为可能。本文提出一种基于自训练的三维医学图像分割无监督域适应框架COSMOS，并通过高分T2磁共振成像（MRI）上前庭神经鞘瘤（VS）与耳蜗的自动分割进行验证。我们的目标感知对比转换网络将源域标注的T1 MRI转换为伪T2 MRI，从而在目标域上实现分割训练，同时保留转换图像中重要的解剖特征。随后通过迭代自训练将未标注数据纳入训练，逐步提升伪标签质量，进而改善分割性能。COSMOS在由第24届国际医学图像计算与计算机辅助介入会议（MICCAI 2021）联合举办的跨模态域适应（crossMoDA）挑战赛中荣获第一名。在VS上，平均Dice系数与平均对称表面距离分别为0.871（0.063）和0.437（0.270）；在耳蜗上则分别为0.842（0.020）和0.152（0.030）。