Data-derived models for segmentation with application to surgical assessment and training

Balakrishnan Varadarajan, Carol Reiley, Henry Lin, Sanjeev Khudanpur, Gregory Hager

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper addresses automatic skill assessment in robotic minimally invasive surgery. Hidden Markov models (HMMs) are developed for individual surgical gestures (or surgemes) that comprise a typical bench-top surgical training task. It is known that such HMMs can be used to recognize and segment surgemes in previously unseen trials [1]. Here, the topology of each surgeme HMM is designed in a data-driven manner, mixing trials from multiple surgeons with varying skill levels, resulting in HMM states that model skill-specific sub-gestures. The sequence of HMM states visited while performing a surgeme are therefore indicative of the surgeon's skill level. This expectation is confirmed by the average edit distance between the state-level "transcripts" of the same surgeme performed by two surgeons with different expertise levels. Some surgemes are further shown to be more indicative of skill than others.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings
Pages426-434
Number of pages9
EditionPART 1
DOIs
StatePublished - 2009
Event12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009 - London, United Kingdom
Duration: Sep 20 2009Sep 24 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5761 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
CountryUnited Kingdom
CityLondon
Period9/20/099/24/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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