Dcl-Net:用于半监督多器官分割的双对比学习网络

L. Wen, Zheng-Kai Feng, Yun Hou, Peng Wang, Xi Wu, Jiliu Zhou, Yan Wang
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引用次数: 0

摘要

半监督学习(SSL)是一种有效的措施,可以缓解对大量标注数据集的严格要求,尤其适用于具有挑战性的多器官分割。然而,大多数现有的半监督学习方法都是独立预测单幅图像中的像素,忽略了图像和类别之间的关系。在本文中,我们提出了一种用于半监督 MoS 的两阶段双对比学习网络,它利用全局和局部对比学习来加强图像和类别之间的关系。具体来说,在第一阶段,我们开发了一种相似性引导的全局对比学习,以探索图像之间隐含的连续性和相似性,并学习全局上下文。然后,在第二阶段,我们提出了器官感知局部对比学习,以进一步吸引类表征。为了减轻计算负担,我们引入了一种掩码中心计算算法来压缩局部对比学习的类别表征。在 2017 年公开的 ACDC 数据集和内部的 RC-OARs 数据集上进行的实验证明了我们的方法性能优越。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation
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.
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