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 proceedingChapter

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. 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 ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages426-434
Number of pages9
Volume12
EditionPt 1
StatePublished - 2009

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Gestures
Minimally Invasive Surgical Procedures
Robotics
Surgeons

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Varadarajan, B., Reiley, C., Lin, H., Khudanpur, S., & Hager, G. (2009). Data-derived models for segmentation with application to surgical assessment and training. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 12, pp. 426-434)

Data-derived models for segmentation with application to surgical assessment and training. / Varadarajan, Balakrishnan; Reiley, Carol; Lin, Henry; Khudanpur, Sanjeev; Hager, Gregory.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 12 Pt 1. ed. 2009. p. 426-434.

Research output: Chapter in Book/Report/Conference proceedingChapter

Varadarajan, B, Reiley, C, Lin, H, Khudanpur, S & Hager, G 2009, Data-derived models for segmentation with application to surgical assessment and training. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 edn, vol. 12, pp. 426-434.
Varadarajan B, Reiley C, Lin H, Khudanpur S, Hager G. Data-derived models for segmentation with application to surgical assessment and training. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 ed. Vol. 12. 2009. p. 426-434
Varadarajan, Balakrishnan ; Reiley, Carol ; Lin, Henry ; Khudanpur, Sanjeev ; Hager, Gregory. / Data-derived models for segmentation with application to surgical assessment and training. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 12 Pt 1. ed. 2009. pp. 426-434
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