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arXiv · 2403.03512

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

Lu Wen·Zhenghao Feng·Yun Hou·Peng Wang·Xi Wu·Jiliu Zhou·Yan Wang
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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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Lu Wen, Zhenghao Feng, Yun Hou, Peng Wang, Xi Wu, Jiliu Zhou, Yan Wang. 2024-03-06. Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation. https://doi.org/10.1109/icassp48485.2024.10447495

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