Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation

• URL: http://arxiv.org/abs/2403.03512v1
• Date: Wed, 6 Mar 2024 07:39:33 GMT
• Title: Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation
• Authors: Lu Wen, Zhenghao Feng, Yun Hou, Peng Wang, Xi Wu, Jiliu Zhou, Yan Wang
• Abstract summary: We propose a two-stage Dual Contrastive Learning Network for semi-supervised MoS. In Stage 1, we develop a similarity-guided global contrastive learning to explore the implicit continuity and similarity among images. In Stage 2, we present an organ-aware local contrastive learning to further attract the class representations.
• Score: 12.798684146496754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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