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16 March 2020 Open-source platform for automated collection of training data to support video-based feedback in surgical simulators
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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
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
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