PL-Seg: Partially labeled abdominal organ segmentation via classwise orthogonal contrastive learning and progressive self-distillation.

Abstract

Accurate segmentation of abdominal organs in Computed Tomography (CT) scans is crucial for effective lesion diagnosis, radiotherapy planning, and patient follow-up. Although deep learning has shown great performance with fully supervised learning, it requires voxel-level dense annotations that are time-consuming and costly to obtain, especially for multiple organs. In this work, we propose a novel framework PL-Seg, for multi-organ segmentation in abdominal CT scans using partially labeled data, where only a subset of the target organ classes are annotated in each volume. First, we introduce a novel Hardness-Aware Decoupled Foreground Loss (HADFL), which focuses exclusively on annotated organs and dynamically adjusts class weights based on historical segmentation difficulty. Then, we employ a Classwise Orthogonal Contrastive Loss (COCL) to reduce inter-class ambiguity, which serves as a regularization for unlabeled regions. In addition, a Progressive Self-Distillation (PSD) that distills knowledge from deep high-resolution layers to shallower low-resolution levels is proposed to improve the feature learning ability under partial class annotations. Experiments conducted on a dataset with varying class-wise annotation ratios and a real clinical partially labeled dataset demonstrate that: 1) PL-Seg achieves significant performance improvements by leveraging unlabeled categories, 2) Compared with six state-of-the-art methods, PL-Seg achieves superior results with a simpler pipeline and greater computational efficiency, and 3) Under the same annotation cost, PL-Seg outperforms existing semi-supervised methods. Furthermore, we release a partially labeled medical image segmentation codebase and benchmark to boost research on this topic: https://github.com/HiLab-git/PLS4MIS.