Automated Microsurgical Tool Segmentation and Characterization in Intra-Operative Neurosurgical Videos

Abstract

Checklist based routine evaluation of surgical skills in any medical school demands quality time and effort from the supervising expert and is highly influenced by assessor bias. Alternatively, automated video based surgical skill assessment is a simple and viable method to analyse surgical dexterity offline without the need for acute presence of an expert surgeon throughout the surgery. In this paper, a novel approach and results for the automated segmentation of microsurgical instruments from the real-world neurosurgical video dataset was presented. The proposed tool segmentation model showcased mean average precision of 96.7% in detecting, and localizing five surgical instruments from the real-world neurosurgical videos. Accurate detection and characterization of motion features of the microsurgical tool from the novel annotated neurosurgical video dataset forms the key step towards automated surgical skill evaluation. Clinical Relevance- Tool segmentation, localization, and characterization in neurosurgical video, has several applications including assessing surgeons skills, training novice surgeons, understanding critical operating procedures post surgery, characterizing any critical anatomical response to the tool that leads to the success or failure of the surgery, and building models for conducting autonomous robotic surgery. Semantic segmentation, and characterization of the microsurgical tools forms the basis of the modern neurosurgery.