用定向框标注提升不规则病灶分割精度，减少人工标注负担。
OBBSeg: Irregular Lesion Segmentation under Oriented Bounding Box Annotations
- 用定向边界框提供紧凑几何监督，更好适应细长病灶。
- 在13个数据集上达到接近全监督的分割效果。
- 适合标注成本高、病灶形状不规则的医学图像场景。
像素级标注仍是医学图像分割的主要瓶颈，弱监督因此成为有吸引力但约束不足的替代方案。本文提出OBBSeg，一种基于定向边界框（OBB）的中间监督范式，弥合了全监督与弱监督之间的差距。通过联合编码空间范围和方向信息，OBB提供了更契合细长或各向异性病灶的紧凑几何监督，降低了粗略框标注的模糊性。为缓解OBB固有的矩形偏差，引入可微的Mask-to-OBB损失，强制预测掩码与OBB区域间的几何一致性。此外，通过两个互补模块PAFE与DBFE，加入提示驱动的语义引导，增强前景表征并抑制背景干扰。在5种成像模态下的13个数据集上进行的大量实验表明，OBBSeg不仅优于现有弱监督方法，且性能可媲美全监督方法，展现出高效、可扩展的医学图像分割潜力。代码已开源：https://github.com/StarLxc3/OBBSeg。
原文摘要 · Abstract (English)
Pixel-level annotation remains a major bottleneck in medical image segmentation, making weak supervision an attractive yet under-constrained alternative. We propose OBBSeg, an intermediate supervision paradigm guided by Oriented Bounding Boxes (OBBs) that bridges the gap between full and weak supervision. By jointly encoding spatial extent and orientation, OBBs provide compact geometric supervision that better aligns with elongated or anisotropic lesions, reducing the ambiguity of coarse box annotations. To mitigate the inherent rectangular bias of OBBs, we introduce a Mask-to-OBB loss, a differentiable formulation that enforces geometric consistency between predicted masks and OBB regions. Furthermore, we incorporate prompt-driven semantic guidance through two complementary modules-PAFE and DBFE-which enhance foreground representation and suppress background interference. Extensive experiments on 13 datasets across 5 imaging modalities show that OBBSeg not only outperforms existing weakly supervised methods but also achieves performance comparable to fully supervised approaches, demonstrating its potential for efficient and scalable medical image segmentation. The code is available at https://github.com/StarLxc3/OBBSeg.
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