FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation

• URL: http://arxiv.org/abs/2306.15189v1
• Date: Tue, 27 Jun 2023 04:14:50 GMT
• Title: FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation
• Authors: Yunsung Chung, Chanho Lim, Chao Huang, Nassir Marrouche, and Jihun Hamm
• Abstract summary: We propose a contrastive learning strategy of foreground and background representations for semi-supervised 3D medical image segmentation. Our framework has the potential to advance the field of semi-supervised 3D medical image segmentation.
• Score: 10.11072886547561
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image segmentation of gadolinium enhancement magnetic resonance imaging (GE MRI) is an important task in clinical applications. However, manual annotation is time-consuming and requires specialized expertise. Semi-supervised segmentation methods that leverage both labeled and unlabeled data have shown promise, with contrastive learning emerging as a particularly effective approach. In this paper, we propose a contrastive learning strategy of foreground and background representations for semi-supervised 3D medical image segmentation (FBA-Net). Specifically, we leverage the contrastive loss to learn representations of both the foreground and background regions in the images. By training the network to distinguish between foreground-background pairs, we aim to learn a representation that can effectively capture the anatomical structures of interest. Experiments on three medical segmentation datasets demonstrate state-of-the-art performance. Notably, our method achieves a Dice score of 91.31% with only 20% labeled data, which is remarkably close to the 91.62% score of the fully supervised method that uses 100% labeled data on the left atrium dataset. Our framework has the potential to advance the field of semi-supervised 3D medical image segmentation and enable more efficient and accurate analysis of medical images with a limited amount of annotated labels.

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