Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
Abstract
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 teams submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of algorithms. This novel benchmark can be used for comparable evaluation and validation of future work.
Keywords
Cite
@article{arxiv.2109.14956,
title = {Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark},
author = {Martin Wagner and Beat-Peter Müller-Stich and Anna Kisilenko and Duc Tran and Patrick Heger and Lars Mündermann and David M Lubotsky and Benjamin Müller and Tornike Davitashvili and Manuela Capek and Annika Reinke and Tong Yu and Armine Vardazaryan and Chinedu Innocent Nwoye and Nicolas Padoy and Xinyang Liu and Eung-Joo Lee and Constantin Disch and Hans Meine and Tong Xia and Fucang Jia and Satoshi Kondo and Wolfgang Reiter and Yueming Jin and Yonghao Long and Meirui Jiang and Qi Dou and Pheng Ann Heng and Isabell Twick and Kadir Kirtac and Enes Hosgor and Jon Lindström Bolmgren and Michael Stenzel and Björn von Siemens and Hannes G. Kenngott and Felix Nickel and Moritz von Frankenberg and Franziska Mathis-Ullrich and Lena Maier-Hein and Stefanie Speidel and Sebastian Bodenstedt},
journal= {arXiv preprint arXiv:2109.14956},
year = {2021}
}