Distribution Alignment Informed Thresholding for Semi-Supervised Curvilinear Structure Segmentation

Abstract

Curvilinear structure segmentation using deep neural networks is often limited by the high cost of annotation. Semi-supervised learning (SSL) helps mitigate this dependency on extensive annotated data. State-of-the-art SSL approaches generate pseudo-labels for unlabeled data, which are then used for further model training. These methods primarily focus on calibrating thresholds to binarize the predictions. In this work, we assume that when labeled and unlabeled data are similar, the foreground-to-background ratio should be consistent between them. To leverage this assumption, we calibrate the threshold by minimizing the distribution gap between labeled ground truth and pseudo-labels on unlabeled data. Our proposed threshold calibration can be integrated with existing SSL methods. We evaluate its effectiveness on four datasets, demonstrating that our method outperforms current state-of-the-art SSL techniques, especially in scenarios with very low labeled data.