TY - GEN
T1 - Critical View of Safety Assessment in Laparoscopic Cholecystectomy via Segment Anything Model
AU - Li, Yunfan
AU - Ling, Haibin
AU - Ramakrishnan, I. V.
AU - Prasanna, Prateek
AU - Sasson, Aaron
AU - Gupta, Himanshu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Laparoscopic Cholecystectomy (LC) is a minimally invasive surgery for the removal of diseased gallbladders. Compared to traditional open cholecystectomy, LC procedures is associated with significantly shorter recovery period, but has an increased chance of bile duct injuries (BDIs). Critical view of safety (CVS) is an important validation method and safety protocol which has a set of conditions that can be visually identified during LC surgeries. In this paper, we approach the problem of automated CVS prediction by combining state-of-the-art object detection methods and prompting the Segment Anything model to achieve more accurate localization of anatomical structures and classification of CVS conditions. When evaluated on our dataset of 5,750 frames with CVS annotations, our method achieved competitive results on frame-level CVS condition prediction, and around 20% improvement on video-level CVS assessment compared to previous SoTA LG-CVS.
AB - Laparoscopic Cholecystectomy (LC) is a minimally invasive surgery for the removal of diseased gallbladders. Compared to traditional open cholecystectomy, LC procedures is associated with significantly shorter recovery period, but has an increased chance of bile duct injuries (BDIs). Critical view of safety (CVS) is an important validation method and safety protocol which has a set of conditions that can be visually identified during LC surgeries. In this paper, we approach the problem of automated CVS prediction by combining state-of-the-art object detection methods and prompting the Segment Anything model to achieve more accurate localization of anatomical structures and classification of CVS conditions. When evaluated on our dataset of 5,750 frames with CVS annotations, our method achieved competitive results on frame-level CVS condition prediction, and around 20% improvement on video-level CVS assessment compared to previous SoTA LG-CVS.
KW - Critical View of Safety
KW - Laparoscopic Cholecystectomy
KW - Object Detection
KW - Segment Anything Model
UR - https://www.scopus.com/pages/publications/85215012888
U2 - 10.1109/EMBC53108.2024.10781674
DO - 10.1109/EMBC53108.2024.10781674
M3 - Conference contribution
C2 - 40031478
AN - SCOPUS:85215012888
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
BT - 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024
Y2 - 15 July 2024 through 19 July 2024
ER -