SDC-UDA：用于切片方向连续跨模态医学图像分割的体素无监督域适应框架
SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation
AI总结：
本文提出 SDC-UDA 体素无监督域适应框架，结合切片内和切片间自注意力图像转换、不确定性约束伪标签细化与体素自训练，解决跨模态医学图像分割问题，实现切片方向连续分割并达到最先进性能。
摘要：
近期基于深度学习的医学图像分割研究在全监督方式下达到了近乎人类水平的性能。然而，在医学成像领域，获取像素级专家标注极其昂贵且费力。无监督域适应（UDA）可以缓解这一问题，它使得利用一种成像模态中的标注数据来训练网络成为可能，该网络能够在无标签的目标成像模态上成功执行分割。在本研究中，我们提出了 SDC-UDA，这是一个简单但有效的体素 UDA 框架，用于切片方向连续的跨模态医学图像分割，它结合了切片内和切片间的自注意力图像转换、不确定性约束的伪标签细化以及体素自训练。我们的方法有别于以往医学图像分割的 UDA 方法，它能够在切片方向上获得连续的分割结果，从而确保更高的准确性及临床实践中的潜力。我们使用多个公开的跨模态医学图像分割数据集验证了 SDC-UDA，并实现了最先进的分割性能，且与以往研究相比，预测在切片方向上具有更优的连续性。
英文摘要：
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.
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