Partially-supervised multi-organ medical image segmentation aims to develop a unified semantic segmentation model by utilizing multiple partially-labeled datasets, with each dataset providing labels for a single class of organs. However, the limited availability of labeled foreground organs and the absence of supervision to distinguish unlabeled foreground organs from the background pose a significant challenge, which leads to a distribution mismatch between labeled and unlabeled pixels. Although existing pseudo-labeling methods can be employed to learn from both labeled and unlabeled pixels, they are prone to performance degradation in this task, as they rely on the assumption that labeled and unlabeled pixels have the same distribution. In this paper, to address the problem of distribution mismatch, we propose a labeled-to-unlabeled distribution alignment (LTUDA) framework that aligns feature distributions and enhances discriminative capability. Specifically, we introduce a cross-set data augmentation strategy, which performs region-level mixing between labeled and unlabeled organs to reduce distribution discrepancy and enrich the training set. Besides, we propose a prototype-based distribution alignment method that implicitly reduces intra-class variation and increases the separation between the unlabeled foreground and background. This can be achieved by encouraging consistency between the outputs of two prototype classifiers and a linear classifier. Extensive experimental results on the AbdomenCT-1K dataset and a union of four benchmark datasets (including LiTS, MSD-Spleen, KiTS, and NIH82) demonstrate that our method outperforms the state-of-the-art partially-supervised methods by a considerable margin, and even surpasses the fully-supervised methods. The source code is publicly available at .

中文翻译：

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用于部分监督多器官医学图像分割的标记与未标记分布对齐


部分监督多器官医学图像分割旨在利用多个部分标记数据集开发统一的语义分割模型，每个数据集为单类器官提供标签。然而，标记的前景器官的可用性有限以及缺乏区分未标记的前景器官与背景的监督构成了重大挑战，这导致标记和未标记像素之间的分布不匹配。尽管现有的伪标记方法可用于从标记和未标记像素中学习，但它们在此任务中很容易出现性能下降，因为它们依赖于标记和未标记像素具有相同分布的假设。在本文中，为了解决分布不匹配的问题，我们提出了一种标记到未标记的分布对齐（LTUDA）框架，该框架可以对齐特征分布并增强判别能力。具体来说，我们引入了一种跨集数据增强策略，该策略在标记和未标记器官之间执行区域级混合，以减少分布差异并丰富训练集。此外，我们提出了一种基于原型的分布对齐方法，该方法隐式地减少了类内变异并增加了未标记的前景和背景之间的分离。这可以通过鼓励两个原型分类器和线性分类器的输出之间的一致性来实现。 AbdomenCT-1K 数据集和四个基准数据集（包括 LiTS、MSD-Spleen、KiTS 和 NIH82）的联合的大量实验结果表明，我们的方法在很大程度上优于最先进的部分监督方法，甚至超越了完全监督的方法。 源代码可在 公开获取。