Development of a Visual Guidance System for Laparoscopic Surgical Palpation using Computer Vision

• Main Authors: Caballas, Kerwin G, Bolingot, Harold Jay, Libatique, Nathaniel Joseph C, Tangonan, Gregory L
• Format: text
• Published: Archīum Ateneo 2021
• Subjects: Laparoscope; Visualization; Minimally invasive surgery; Computational modeling; Decision making; Biological systems; Bladder; Laparoscopic palpation; surgical computer vision; instance segmentation; image-guided surgery; artificial intelligence; surgical decision-making; Electrical and Computer Engineering; Surgery
• Online Access: https://archium.ateneo.edu/ecce-faculty-pubs/123; https://ieeexplore.ieee.org/document/9398796
• Institution: Ateneo De Manila University

Currently, there are numerous obstacles to performing palpation during laparoscopic surgery. The laparoscopic interface does not allow access into a patient's body anything other than the tools that are inserted through small incisions. Palpation is a useful technique for augmenting surgical decision-making during laparoscopic surgery, especially when discerning operations involving cancerous tumors. In this study, a visual guidance system is proposed for use during laparoscopic palpation, specifically engineered to be part of a motion-based laparoscopic palpation technique. In particular, the YOLACT++ model is used to localize a target organ, the gall bladder, on a custom dataset of laparoscopic cholecystectomy. Our experiments showed an AP score of 90.10 for bounding boxes and 87.20 on masks. In terms of the speed performance, the model achieved a playback speed of approximately 20 fps, which translates to approximately 48 ms video latency. The palpation path guides are guidelines that are computer-generated within the identified organ, and show potential in helping the surgeon implement the palpation more accurately. Overall, this study demonstrates the potential of deep learning-based real-time image processing models to complete our motion-based laparoscopic palpation system, and to realize the promising role of artificial intelligence in surgical decision-making.