BoxSegUS: A Spatial Consistency Box Supervised Multi-Class Segmentation with Prior and Boundary Constraint for Ultrasound Images.

Abstract

Accurate segmentation of anatomical structures in ultrasound images is essential for quantitative assessment, fetal development monitoring, and disease diagnosis. While fully supervised models have achieved strong performance, their reliance on costly pixel-level annotations has driven interest in box-based weakly supervised methods. However, existing medical box-supervised approaches are predominantly designed for single-class segmentation and remain less effective for complex multi-structure ultrasound images. These methods struggle due to inherent characteristics such as spatial variations, unreliable local appearance cues, and fuzzy boundaries. Moreover, the dense spatial distribution of anatomical structures leads to heavily overlapping boxes and class-assignment ambiguity. In this study, we propose BoxSegUS, a box-supervised framework that exploits bounding-box annotations for accurate ultrasound segmentation. Specifically, we enforce weak-strong spatial consistency to improve robustness against spatial variations and employ a detection-prior global context modeling mechanism to reduce the influence of unreliable local appearance cues. To mitigate class-assignment ambiguity caused by overlapping anatomical boxes, we incorporate inter-/intra-class anatomical priors to guide mask evolution with coarse structural knowledge. Finally, a Soft Projection loss and sparse boundary regularization are used to handle imperfect box annotations and refine boundaries. Experiments on four ultrasound datasets, including three multi-structure datasets and one external thyroid nodule dataset, show that BoxSegUS consistently outperforms representative box-supervised baselines and achieves competitive performance compared with fully supervised models.