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

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.