FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation
This addresses the need for efficient and accurate medical image segmentation with limited annotations, which is crucial for clinical applications but incremental in the context of semi-supervised learning.
The paper tackles the problem of semi-supervised 3D medical image segmentation by proposing a contrastive learning strategy that distinguishes foreground and background regions, achieving a Dice score of 91.31% with only 20% labeled data, close to the 91.62% of fully supervised methods.
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.