Open-source platform for automated collection of training data to support video-based feedback in surgical simulators

Jacob Laframboise; Tamas Ungi; Kyle Sunderland; Boris Zevin; Gabor Fichtinger

doi:10.1117/12.2549878

16 March 2020 Open-source platform for automated collection of training data to support video-based feedback in surgical simulators

Jacob Laframboise, Tamas Ungi, Kyle Sunderland, Boris Zevin, Gabor Fichtinger

Author Affiliations +

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 1131517 (2020) https://doi.org/10.1117/12.2549878
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Purpose: Surgical training could be improved by automatic detection of workflow steps, and similar applications of image processing. A platform to collect and organize tracking and video data would enable rapid development of image processing solutions for surgical training. The purpose of this research is to demonstrate 3D Slicer / PLUS Toolkit as a platform for automatic labelled data collection and model deployment. Methods: We use PLUS and 3D Slicer to collect a labelled dataset of tools interacting with tissues in simulated hernia repair, comprised of optical tracking data and video data from a camera. To demonstrate the platform, we train a neural network on this data to automatically identify tissues, and the tracking data is used to identify what tool is in use. The solution is deployed with a custom Slicer module. Results: This platform allowed the collection of 128,548 labelled frames, with 98.5% correctly labelled. A CNN was trained on this data and applied to new data with an accuracy of 98%. With minimal code, this model was deployed in 3D Slicer on real-time data at 30fps. Conclusion: We found the 3D Slicer and PLUS Toolkit platform to be a viable platform for collecting labelled training data and deploying a solution that combines automatic video processing and optical tool tracking. We designed an accurate proof-of-concept system to identify tissue-tool interactions with a trained CNN and optical tracking.

Conference Presentation

Citation Download Citation

Jacob Laframboise, Tamas Ungi, Kyle Sunderland, Boris Zevin, and Gabor Fichtinger "Open-source platform for automated collection of training data to support video-based feedback in surgical simulators", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131517 (16 March 2020); https://doi.org/10.1117/12.2549878

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available