Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
Medical ultrasound imaging is ubiquitous, but manual analysis struggles to
keep pace. Automated segmentation can help but requires large labeled datasets,
which are scarce. Semi-supervised learning leveraging both unlabeled and
limited labeled data is a promising approach. State-of-the-art methods use
consistency regularization or pseudo-labeling but grow increasingly complex.
Without sufficient labels, these models often latch onto artifacts or allow
anatomically implausible segmentations. In this paper, we present a simple yet
effective pseudo-labeling method with an adversarially learned shape prior to
regularize segmentations. Specifically, we devise an encoder-twin-decoder
network where the shape prior acts as an implicit shape model, penalizing
anatomically implausible but not ground-truth-deviating predictions. Without
bells and whistles, our simple approach achieves state-of-the-art performance
on two benchmarks under different partition protocols. We provide a strong
baseline for future semi-supervised medical image segmentation. Code is
available at https://github.com/WUTCM-Lab/Shape-Prior-Semi-Seg.
