Problem
Research questions and friction points this paper is trying to address.
Surgical Instrument Segmentation
Reasoning-Aware
Contextual Understanding
SIRA: Reasoning-Aware Surgical Instrument Segmentation via Query-Anchored Alignment
🤖 AI Summary
本文通过引入基于查询锚定对齐的手术器械推理分割助手SIRA，解决了现有方法在手术流程中捕捉程序上下文和任务依赖语义能力有限的问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Reasoning-Aware Surgical Instrument Segmentation
Query-Anchored Alignment
Surgical Reasoning Segmentation Dataset
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