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Hand Motion-Aware Surgical Tool Localization and Classification from an Egocentric Camera.
Tomohiro Shimizu1, Ryo Hachiuma1, Hiroki Kajita2
1Faculty of Science and Technology, Keio University, Yokohama, Kanagawa 223-8852, Japan.
Journal of Imaging
|August 30, 2021
Summary
This study introduces a novel method for detecting surgical tools in open surgery videos by analyzing hand movements. The approach accurately distinguishes between similar tools like scissors and needle holders, improving surgical video analysis.
Area of Science:
- Computer Vision
- Surgical Robotics
- Medical Imaging Analysis
Background:
- Detecting surgical tools in open surgery is challenging due to similar tool shapes and obscured views.
- Traditional methods struggle with low camera resolution and hidden tool tips in open surgical environments.
Purpose of the Study:
- To develop an automated system for detecting and classifying surgical tools in open surgery videos.
- To leverage hand movement data for differentiating between visually similar surgical instruments.
Main Methods:
- A three-module system combining Faster R-CNN for localization and ResNet-18 with LSTM for classification based on hand movements.
- Development of a dataset with annotated surgical tool detections from seven types of open surgery.
Main Results:
- The proposed method successfully detected and classified two types of surgical tools (scissors and needle holders).
- The approach demonstrated superior performance compared to two baseline methods in surgical tool detection.
Conclusions:
- Hand movement analysis combined with deep learning effectively addresses the challenges of surgical tool detection in open surgery.
- This technique offers a promising advancement for surgical video analysis and evaluation systems.
