Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation (UDA) 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 SDC-UDA, a simple yet effective volumetric UDA framework for slice-direction continuous cross-modality medical image segmentation which combines intra- and inter-slice self-attentive image translation, uncertainty-constrained pseudo-label refinement, and volumetric self-training. Our method is distinguished from previous methods on UDA for medical image segmentation in that it can obtain continuous segmentation in the slice direction, thereby ensuring higher accuracy and potential in clinical practice. We validate SDC-UDA with multiple publicly available cross-modality medical image segmentation datasets and achieve state-of-the-art segmentation performance, not to mention the superior slice-direction continuity of prediction compared to previous studies.

翻译：近期基于深度学习的医学图像分割研究在全监督方式下实现了接近人类水平的分割性能。然而在医学影像领域，获取像素级专家标注极为昂贵且耗时。无监督域适应（UDA）可缓解该问题，使得利用某一成像模态的标注数据训练网络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SDC-UDA——一种简单而有效的体积UDA框架，专用于切片方向连续跨模态医学图像分割。该框架融合了切片内与切片间的自注意力图像翻译、不确定性约束伪标签精炼以及体积自训练。与以往医学图像分割UDA方法不同，本方法可获取切片方向上的连续分割，从而确保更高的精度与临床实践潜力。我们通过多个公开的跨模态医学图像分割数据集验证了SDC-UDA，不仅实现了最先进的分割性能，更在预测的切片方向连续性上显著优于先前研究。