Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

Abstract

The data consists of endoscopic videos from general surgery operating rooms. The data was obtained during laparoscopic surgeries at the University Hospital of Heidelberg and its affiliate hospitals, forming a joint center of excellence for minimally invasive surgery. All surgeries were annotated framewise for surgical phases by surgical experts. Furthermore certain surgical actions, instrument usage and surgical skill levels were annotated. The surgeries recorded are laparoscopic gallbladder removals (cholecystectomy).

The dataset consists out of at least 30 different recorded surgeries from three hospitals. For each surgery, the video captured by the endoscope is provided. To ensure anonymity, frames corresponding to extra-abdominal views are censored by entirely white (RGB 255 255 255) frames.

The data will be released in three different sets: 2 training sets (the first set containing at least 12 videos and the second set containing at least 12 videos), which include framewise annotation of surgical phase, instrument usage, actions of surgeon and assistant as well as surgical skill. A testing set consisting of at least 6 videos will be provided.