BoundarySeg:Boundary-Aware Multi-Task Learning for Semi-Supervised 3D Medical Image Segmentation with Limited Unlabeled Data

Data scarcity is a primary bottleneck in 3D medical image segmentation: expert voxel-level annotations are expensive, and privacy regulations often severely restrict access to unannotated clinical volumes. While semi-supervised learning (SSL) aims to alleviate annotation burdens, existing paradigms implicitly rely on large pools of unlabeled data (U) relative to labeled data (L), assuming U gg L to filter noise and enforce consistency. When restricted to the data-constrained regime where unlabeled data are limited (U ≤ L), standard SSL methods suffer from severe pseudo-label degeneracy, performance degradation, and high seed-to-seed variance. In this work, we propose BoundarySeg, a unified multi-task semi-supervised framework specifically designed for the limited unlabeled data regime (U ≤ L). BoundarySeg formulates volumetric segmentation as a joint learning task that predicts whole-organ geometry alongside auxiliary organ boundaries. Rather than relying on large cohorts to average out pseudo-label noise, BoundarySeg employs the auxiliary boundary branch as an internal geometric anchor. Coupled with uncertainty-weighted mean-teacher consistency across both tasks alongside axis-flip equivariance and copy-paste regularization, this joint supervision stabilizes spatial representations and prevents segmentation collapse when U ≤ L. Across multiple 3D segmentation datasets, BoundarySeg maintains high performance stability as unlabeled pool size shrinks, consistently outperforming state-of-the-art semi-supervised frameworks in low-data settings. hrefhttps://github.com/tushaarkataria/BoundarySeghttps://github.com/tushaarkataria/BoundarySeg