Instructional cataract surgery videos are crucial for ophthalmologists and trainees to observe surgical details repeatedly. This paper presents a deep learning model for real-time identification of surgical instruments in these videos, using a custom dataset scraped from open-access sources. Inspired by the architecture of YOLOV9, the model employs a Programmable Gradient Information (PGI) mechanism and a novel Generally-Optimized Efficient Layer Aggregation Network (Go-ELAN) to address the information bottleneck problem, enhancing Minimum Average Precision (mAP) at higher Non-Maximum Suppression Intersection over Union (NMS IoU) scores. The Go-ELAN YOLOV9 model, evaluated against YOLO v5, v7, v8, v9 vanilla, Laptool and DETR, achieves a superior mAP of 73.74 at IoU 0.5 on a dataset of 615 images with 10 instrument classes, demonstrating the effectiveness of the proposed model.
@article{arxiv.2501.02618,
title = {Identifying Surgical Instruments in Pedagogical Cataract Surgery Videos through an Optimized Aggregation Network},
author = {Sanya Sinha and Michal Balazia and Francois Bremond},
journal= {arXiv preprint arXiv:2501.02618},
year = {2025}
}
Comments
Preprint. Full paper accepted at the IEEE International Conference on Image Processing Applications and Systems (IPAS), Lyon, France, Jan 2025. 6 pages