  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.
