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

TL;DR

FBA-Net introduces a contrastive learning approach focusing on foreground and background representations to improve semi-supervised 3D medical image segmentation, achieving near fully supervised performance with limited labeled data.

Contribution

The paper proposes a novel contrastive learning strategy for foreground and background representations in semi-supervised 3D medical image segmentation, enhancing performance with fewer labels.

Findings

Achieves 91.31% Dice score with only 20% labeled data.

Outperforms existing semi-supervised methods on three datasets.

Close to fully supervised performance with limited labels.

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.