发表机构
School of Electronic Information and Communications; Huazhong University of Science and Technology（电子信息与通信学院; 华中科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有手术器械分割缺乏程序上下文的问题，提出RA-SIS任务及SurgRS数据集，并设计SIRA多模态框架，通过查询锚定双对齐提升语义-视觉一致性，实验验证其优于推理感知基线。
AI 中文摘要
手术器械分割（SIS）在机器人辅助和手术流程分析中起着关键作用。然而，大多数现有的SIS方法将分割表述为一个类别驱动的定位问题，限制了它们在手术流程中捕捉程序上下文和任务相关语义的能力。我们引入了推理感知的手术器械分割（RA-SIS），这是一种将分割表述为手术上下文下的查询条件推理的任务公式。为了对此设置进行基准测试，我们构建了SurgRS，一个包含41,000个图像-文本对的手术推理分割数据集，该数据集将实例级掩码与结构化的查询-答案监督对齐，以实现像素级别的语义锚定。基于SurgRS，我们提出了手术器械推理与分割助手（SIRA），一个多模态框架，该框架将目标级和查询级语义分离，并通过查询锚定的双对齐将它们与视觉特征集成。通过将查询语义与空间特征和分割提示对齐，SIRA增强了掩码预测中的语义-视觉一致性。在SurgRS上进行的大量实验表明，与现有的推理感知基线相比，性能有所提升。代码可在以下网址获取：此https URL。
英文摘要
Surgical instrument segmentation (SIS) plays a critical role in robotic assistance and surgical workflow analysis. However, most existing SIS methods formulate segmentation as a category-driven localization problem, limiting their ability to capture procedural context and task-dependent semantics in surgical workflows. We introduce Reasoning-Aware Surgical Instrument Segmentation (RA-SIS), a task formulation that frames segmentation as query-conditioned inference under surgical context. To benchmark this setting, we construct SurgRS, a surgical reasoning segmentation dataset consisting of 41,000 image-text pairs, which aligns instance-level masks with structured query-answer supervision to enable semantic grounding at the pixel level. Based on SurgRS, we propose Surgical Instrument Reasoning and Segmentation Assistant (SIRA), a multimodal framework that disentangles target-level and query-level semantics and integrates them with visual features through query-anchored dual alignment. By aligning query semantics with spatial features and segmentation prompts, SIRA enhances semantic-visual consistency in mask prediction. Extensive experiments on SurgRS demonstrate improvements over existing reasoning-aware baselines. Code is available at https://github.com/linxir226/SIRA.
