Chain-of-Look Spatial Reasoning for Dense Surgical Instrument Counting
Abstract
Accurate counting of surgical instruments in Operating Rooms (OR) is a critical prerequisite for ensuring patient safety during surgery. Despite recent progress of large visual-language models and agentic AI, accurately counting such instruments remains highly challenging, particularly in dense scenarios where instruments are tightly clustered. To address this problem, we introduce Chain-of-Look, a novel visual reasoning framework that mimics the sequential human counting process by enforcing a structured visual chain, rather than relying on classic object detection which is unordered. This visual chain guides the model to count along a coherent spatial trajectory, improving accuracy in complex scenes. To further enforce the physical plausibility of the visual chain, we introduce the neighboring loss function, which explicitly models the spatial constraints inherent to densely packed surgical instruments. We also present SurgCount-HD, a new dataset comprising 1,464 high-density surgical instrument images. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches for counting (e.g., CountGD, REC) as well as Multimodality Large Language Models (e.g., Qwen, ChatGPT) in the challenging task of dense surgical instrument counting.
Keywords
Cite
@article{arxiv.2602.11024,
title = {Chain-of-Look Spatial Reasoning for Dense Surgical Instrument Counting},
author = {Rishikesh Bhyri and Brian R Quaranto and Philip J Seger and Kaity Tung and Brendan Fox and Gene Yang and Steven D. Schwaitzberg and Junsong Yuan and Nan Xi and Peter C W Kim},
journal= {arXiv preprint arXiv:2602.11024},
year = {2026}
}
Comments
Accepted to WACV 2026. This version includes additional authors who contributed during the rebuttal phase