title: Performance comparison of various feature detector-descriptors and temporal models for video-based assessment of laparoscopic skills.
abstract: BACKGROUND
  Despite the significant progress in hand gesture analysis for surgical skills assessment, video-based analysis has not received much attention. In this study we investigate the application of various feature detector-descriptors and temporal modeling techniques for laparoscopic skills assessment.
  
  
  METHODS
  Two different setups were designed: static and dynamic video-histogram analysis. Four well-known feature detection-extraction methods were investigated: SIFT, SURF, STAR-BRIEF and STIP-HOG. For the dynamic setup two temporal models were employed (LDS and GMMAR model). Each method was evaluated for its ability to classify experts and novices on peg transfer and knot tying.
  
  
  RESULTS
  STIP-HOG yielded the best performance (static: 74-79%; dynamic: 80-89%). Temporal models had equivalent performance. Important differences were found between the two groups with respect to the underlying dynamics of the video-histogram sequences.
  
  
  CONCLUSIONS
  Temporal modeling of feature histograms extracted from laparoscopic training videos provides information about the skill level and motion pattern of the operator. Copyright © 2015 John Wiley & Sons, Ltd.
authors.affiliation: Medical Physics Lab-Simulation Center, School of Medicine, University of Athens, Greece.
authors.forename: Constantinos
authors.initials: C
authors.lastname: Loukas
authors.affiliation: Medical Physics Lab-Simulation Center, School of Medicine, University of Athens, Greece.
authors.forename: Evangelos
authors.initials: E
authors.lastname: Georgiou
mesh_terms: D002983:Clinical Competence; D006801:Humans; D010535:Laparoscopy / Q000295:instrumentation / Q000379:methods*; D014741:Video Recording
keywords: feature extraction; laparoscopy; simulation; skills assessment; surgery; video processing