OBBSeg: Irregular Lesion Segmentation under Oriented Bounding Box Annotations

Pixel-level annotation remains a major bottleneck in medicalimage segmentation, making weak supervision an attractive yet under-constrained alternative. We propose OBBSeg, an intermediate super-vision paradigm guided by Oriented Bounding Boxes (OBBs) thatbridges the gap between full and weak supervision. By jointly encodingspatial extent and orientation, OBBs provide compact geometric super-vision that better aligns with elongated or anisotropic lesions, reducingthe ambiguity of coarse box annotations. To mitigate the inherent rect-angular bias of OBBs, we introduce a Mask-to-OBB loss, a differen-tiable formulation that enforces geometric consistency between predictedmasks and OBB regions. Furthermore, we incorporate prompt-drivensemantic guidance through two complementary modules—PAFE andDBFE—which enhance foreground representation and suppress back-ground interference. Extensive experiments on 13 datasets across 5imaging modalities show that OBBSeg not only outperforms existingweakly supervised methods but also achieves performance comparableto fully supervised approaches, demonstrating its potential for efficientand scalable medical image segmentation. The code is available athttps://github.com/StarLxc3/OBBSeg.