An Evidence-Based Tri-Branch Cross-Pseudo Supervision Method for Semi-Supervised Medical Image Segmentation

Abstract

The semi-supervised medical image segmentation with a few annotated data can provide significant help in robot-assisted surgery. This step plays a pivotal role in identification of pathological regions, more appropriate planning of surgical procedures, and so on. In this work, we develop an evidence-based tri-branch cross-pseudo supervision model, which integrates evidence-based uncertainty estimation and multi-branch cross supervision to bolster the effectiveness of semi-supervised learning. The overall framework consists of a vanilla network and an evidential dual-branch network. Two evidential branches EPB and ERB are proposed to complement each other and improve the quality of pseudo-labels. The EPB places more focus on classification accuracy at the pixel level and the ERB emphasizes the similarity and overall integrity of the segmented regions. Then, a novel cross-pseudo supervision strategy among the three branches is designed, to guarantee that valuable and diverse unlabeled knowledge is explored and transferred for segmentation improvement. The effectiveness of the proposed method was verified on the ACDC dataset, achieving outstanding performance compared with other state-of-the-art methods. In addition, we conducted ablation study to validate the effectiveness of the evidential branches (EPB and ERB) and tri-branch cross-supervision strategy, respectively.