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arXiv · 2609.21402

SIRA: Reasoning-Aware Surgical Instrument Segmentation via Query-Anchored Alignment

Zhibo Zhang·Qijie Wang·Zengqiang Yan
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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.

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Zhibo Zhang, Qijie Wang, Zengqiang Yan. 2026-09-18. SIRA: Reasoning-Aware Surgical Instrument Segmentation via Query-Anchored Alignment. https://arxiv.org/abs/2609.21402

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