FBA-Net: Foreground and Background Aware Contrastive Learning for
  Semi-Supervised Atrium Segmentation
Yunsung Chung Chanho Lim Chao Huang Nassir Marrouche Jihun Hamm
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.