Surgment: Segmentation-enabled Semantic Search and Creation of Visual Question and Feedback to Support Video-Based Surgery Learning

• URL: http://arxiv.org/abs/2402.17903v1
• Date: Tue, 27 Feb 2024 21:42:23 GMT
• Title: Surgment: Segmentation-enabled Semantic Search and Creation of Visual Question and Feedback to Support Video-Based Surgery Learning
• Authors: Jingying Wang, Haoran Tang, Taylor Kantor, Tandis Soltani, Vitaliy Popov and Xu Wang
• Abstract summary: Surgment is a system that helps expert surgeons create exercises with feedback based on surgery recordings. The segmentation pipeline enables functionalities to create visual questions and feedback desired by surgeons. 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.
• Score: 4.509082876666929
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Thoracic Surgery Video Analysis for Surgical Phase Recognition [0.08706730566331035]
  We analyse and evaluate both frame-based and video clipping-based phase recognition on thoracic surgery dataset consisting of 11 classes of phases. We show that Masked Video Distillation(MVD) exhibits superior performance, achieving a top-1 accuracy of 72.9%, compared to 52.31% achieved by ImageNet ViT.
  arXiv  Detail & Related papers  (2024-06-13T14:47:57Z)
• Deep Multimodal Fusion for Surgical Feedback Classification [70.53297887843802]
  We leverage a clinically-validated five-category classification of surgical feedback. We then develop a multi-label machine learning model to classify these five categories of surgical feedback from inputs of text, audio, and video modalities. The ultimate goal of our work is to help automate the annotation of real-time contextual surgical feedback at scale.
  arXiv  Detail & Related papers  (2023-12-06T01:59:47Z)
• Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures [51.78027546947034]
  Recent advancements in surgical computer vision have been driven by vision-only models, which lack language semantics. We propose leveraging surgical video lectures from e-learning platforms to provide effective vision and language supervisory signals. We address surgery-specific linguistic challenges using multiple automatic speech recognition systems for text transcriptions.
  arXiv  Detail & Related papers  (2023-07-27T22:38:12Z)
• Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose Estimation of Surgical Instruments [66.74633676595889]
  We present a multi-camera capture setup consisting of static and head-mounted cameras. Second, we publish a multi-view RGB-D video dataset of ex-vivo spine surgeries, captured in a surgical wet lab and a real operating theatre. Third, we evaluate three state-of-the-art single-view and multi-view methods for the task of 6DoF pose estimation of surgical instruments.
  arXiv  Detail & Related papers  (2023-05-05T13:42:19Z)
• Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding [53.992124594124896]
  We present a method for live image-only guidance leveraging a large data set of annotated neurosurgical videos. A generated roadmap encodes the common anatomical paths taken in surgeries in the training set. We trained and evaluated the proposed method with a data set of 166 transsphenoidal adenomectomy procedures.
  arXiv  Detail & Related papers  (2023-03-31T12:52:24Z)
• Using Hand Pose Estimation To Automate Open Surgery Training Feedback [0.0]
  This research aims to facilitate the use of state-of-the-art computer vision algorithms for the automated training of surgeons. By estimating 2D hand poses, we model the movement of the practitioner's hands, and their interaction with surgical instruments.
  arXiv  Detail & Related papers  (2022-11-13T21:47:31Z)
• Quantification of Robotic Surgeries with Vision-Based Deep Learning [45.165919577877695]
  We propose a unified deep learning framework, entitled Roboformer, which operates exclusively on videos recorded during surgery. We validated our framework on four video-based datasets of two commonly-encountered types of steps within minimally-invasive robotic surgeries.
  arXiv  Detail & Related papers  (2022-05-06T06:08:35Z)
• CholecTriplet2021: A benchmark challenge for surgical action triplet recognition [66.51610049869393]
  This paper presents CholecTriplet 2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. We present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods and 19 new deep learning algorithms are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%.
  arXiv  Detail & Related papers  (2022-04-10T18:51:55Z)
• ESAD: Endoscopic Surgeon Action Detection Dataset [10.531648619593572]
  We aim to make surgical assistant robots safer by making them aware about the actions of surgeon, so it can take appropriate assisting actions. We introduce a challenging dataset for surgeon action detection in real-world endoscopic videos.
  arXiv  Detail & Related papers  (2020-06-12T13:22:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.