Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes. To address these problems, this paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner with two differently initialized models before using the pseudo-labels generated by one model to supervise the other. Besides, we design an over-expectation cross-entropy loss for filtering the unlabeled pixels to reduce noise in their pseudo-labels. Quantitative and qualitative experiments on three public datasets demonstrate that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods on both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824 and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively.

翻译：医学图像分割在大量标注数据可用时已取得显著进展。然而，由于需要专业技能，标注医学图像分割数据集成本高昂。此外，医学图像中类别分布往往不均，这严重影响了少数类别的分类性能。为解决这些问题，本文提出用于半监督医学图像分割的共分布对齐方法（Co-DA）。具体而言，Co-DA在利用一个模型生成的伪标签监督另一模型之前，通过两个不同初始化的模型以类别方式将未标注数据的边际预测与标注数据的边际预测对齐。此外，我们设计了一种过期望交叉熵损失，用于过滤未标注像素，以减少其伪标签中的噪声。在三个公开数据集上的定量和定性实验表明，所提方法在二维CaDIS数据集以及三维LGE-MRI和ACDC数据集上均优于现有最先进的半监督医学图像分割方法：在CaDIS上仅使用24%标注数据即达到0.8515的mIoU，在LGE-MRI和ACDC上仅使用20%数据分别取得0.8824和0.8773的Dice分数。