Semi-supervised learning is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation . However, most existing SSL methods predict pixels in a single image independently, ignoring the relations among images and categories. In this paper, we propose a two-stage Dual Contrastive Learning Network for semi-supervised MoS, which utilizes global and local contrastive learning to strengthen the relations among images and classes. Concretely, in Stage 1, we develop a similarity-guided global contrastive learning to explore the implicit continuity and similarity among images and learn global context. Then, in Stage 2, we present an organ-aware local contrastive learning to further attract the class representations. To ease the computation burden, we introduce a mask center computation algorithm to compress the category representations for local contrastive learning. Experiments conducted on the public 2017 ACDC dataset and an in-house RC-OARs dataset has demonstrated the superior performance of our method.

翻译：半监督学习是缓解对大量标注数据集严格需求的有效措施，尤其适用于具有挑战性的多器官分割任务。然而，现有的大多数半监督学习方法独立预测单幅图像中的像素，忽略了图像间及类别间的关联。本文提出了一种两阶段双对比学习网络用于半监督多器官分割，通过全局与局部对比学习增强图像与类别间的关联。具体而言，在第一阶段，我们开发了相似性引导的全局对比学习，以探索图像间的隐式连续性和相似性，并学习全局上下文。在第二阶段，我们提出了一种器官感知的局部对比学习，进一步吸引类别表征。为减轻计算负担，我们引入了一种掩模中心计算算法，用于压缩局部对比学习中的类别表征。在公开的2017 ACDC数据集及内部RC-OARs数据集上进行的实验表明，我们的方法具有优越的性能。