AP-Net: Semi-Supervised Ultrasound Cardiac Segmentation Using Enhanced Anatomical Prior

Abstract

Semi-supervised segmentation is gaining popularity in medical image analysis due to challenges in data acquisition and annotation. However, most methods focus on generating additional training pairs from unlabeled data through augmentation or perturbation for contrastive learning, often overlooking the unique characteristics and inherent priors of medical images. We identified two key anatomical priors in fetal cardiac ultrasound images: (1) anatomies have consistent shapes and locations due to standard views captured by sonographers from fixed angles; (2) category pixels are densely clustered, with each structure appearing only once per image. We propose AP-Net, which uses an anatomical prior generation module, a prior-feature fusion module, and a category-aware cropping strategy to effectively leverage these anatomical priors. Experiments on a real-world fetal cardiac ultrasound dataset show that AP-Net outperforms classical supervised and leading semi-supervised methods, with each component enhancing its performance.