Instrument Recognition in Laparoscopy for Technical Skill Assessment

Abstract

Laparoscopic skill training and evaluation as well as identifying technical errors in surgical procedures have become important aspects in Surgical Quality Assessment (SQA). Typically performed in a manual, time-consuming and effortful post-surgical process, evaluating technical skills for a large part involves assessing proper instrument handling as the main cause for these type of errors. Therefore, when attempting to improve upon this situation using computer vision approaches, the automatic identification of instruments in laparoscopy videos is the very first step toward a semi-automatic assessment procedure. Within this work we summarize existing methodologies for instrument recognition, while proposing a state-of-the-art instance segmentation approach. As a first experiment in the domain of gynecology, our approach is able to segment instruments well but a much higher precision will be required, since this early step is critical before attempting any kind of skill recognition.