Surgment: Segmentation-enabled Semantic Search and Creation of Visual Question and Feedback to Support Video-Based Surgery Learning
This work addresses the need for more interactive and targeted learning tools for surgical trainees, though it is incremental as it builds on existing segmentation methods for a specific domain.
The paper tackled the problem of enhancing video-based surgery learning by developing Surgment, a system that uses a few-shot-learning segmentation pipeline (SegGPT+SAM) with 92% accuracy to enable expert surgeons to create visual questions and feedback from surgery recordings, resulting in high educational value as reported by 11 surgeons.
Videos are prominent learning materials to prepare surgical trainees before they enter the operating room (OR). In this work, we explore techniques to enrich the video-based surgery learning experience. We propose Surgment, a system that helps expert surgeons create exercises with feedback based on surgery recordings. Surgment is powered by a few-shot-learning-based pipeline (SegGPT+SAM) to segment surgery scenes, achieving an accuracy of 92\%. The segmentation pipeline enables functionalities to create visual questions and feedback desired by surgeons from a formative study. Surgment enables surgeons to 1) retrieve frames of interest through sketches, and 2) design exercises that target specific anatomical components and offer visual feedback. In an evaluation study with 11 surgeons, participants applauded the search-by-sketch approach for identifying frames of interest and found the resulting image-based questions and feedback to be of high educational value.