GroundedSurg: A Multi-Procedure Benchmark for Language-Conditioned Surgical Tool Segmentation

• URL: http://arxiv.org/abs/2603.01108v1
• Date: Sun, 01 Mar 2026 13:49:53 GMT
• Title: GroundedSurg: A Multi-Procedure Benchmark for Language-Conditioned Surgical Tool Segmentation
• Authors: Tajamul Ashraf, Abrar Ul Riyaz, Wasif Tak, Tavaheed Tariq, Sonia Yadav, Moloud Abdar, Janibul Bashir,
• Abstract summary: We introduce GroundedSurg, the first language-conditioned, instance-level surgical grounding benchmark.<n>The dataset spans ophthalmic, laparoscopic, robotic, and open procedures, encompassing diverse instrument types, imaging conditions, and operative complexities.
• Score: 1.9981885081131854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clinically reliable perception of surgical scenes is essential for advancing intelligent, context-aware intraoperative assistance such as instrument handoff guidance, collision avoidance, and workflow-aware robotic support. Existing surgical tool benchmarks primarily evaluate category-level segmentation, requiring models to detect all instances of predefined instrument classes. However, real-world clinical decisions often require resolving references to a specific instrument instance based on its functional role, spatial relation, or anatomical interaction capabilities not captured by current evaluation paradigms. We introduce GroundedSurg, the first language-conditioned, instance-level surgical grounding benchmark. Each instance pairs a surgical image with a natural-language description targeting a single instrument, accompanied by structured spatial grounding annotations including bounding boxes and point-level anchors. The dataset spans ophthalmic, laparoscopic, robotic, and open procedures, encompassing diverse instrument types, imaging conditions, and operative complexities. By jointly evaluating linguistic reference resolution and pixel-level localization, GroundedSurg enables a systematic and realistic evaluation of vision-language models in clinically realistic multi-instrument scenes. Extensive experiments demonstrate substantial performance gaps across modern segmentation and VLMs, highlighting the urgent need for clinically grounded vision-language reasoning in surgical AI systems. Code and data are publicly available at https://github.com/gaash-lab/GroundedSurg

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