Episode Classification for the Analysis of Tissue/Instrument Interaction with Multiple Visual Cues
Identifieur interne : 000A45 ( Istex/Corpus ); précédent : 000A44; suivant : 000A46Episode Classification for the Analysis of Tissue/Instrument Interaction with Multiple Visual Cues
Auteurs : L. Lo ; Ara Darzi ; Guang-Zhong YangSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2003.
Abstract
Abstract: The assessment of surgical skills for Minimally Invasive Surgery (MIS) has traditionally been conducted with visual observation and objective scoring. This paper presents a practical framework for the detection of instrument/tissue interaction from MIS video sequences by incorporating multiple visual cues. The proposed technique investigates the characteristics of four major events involved in MIS procedures including idle, retraction, cauterisation and suturing. Constant instrument tracking is maintained and multiple visual cues related to shape, deformation, changes in light reflection and other low level images featured are combined in a Bayesian framework to achieve an overall frame-by-frame classification accuracy of 77% and episode classification accuracy of 85%.
Url:
DOI: 10.1007/978-3-540-39899-8_29
Links to Exploration step
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<front><div type="abstract" xml:lang="en">Abstract: The assessment of surgical skills for Minimally Invasive Surgery (MIS) has traditionally been conducted with visual observation and objective scoring. This paper presents a practical framework for the detection of instrument/tissue interaction from MIS video sequences by incorporating multiple visual cues. The proposed technique investigates the characteristics of four major events involved in MIS procedures including idle, retraction, cauterisation and suturing. Constant instrument tracking is maintained and multiple visual cues related to shape, deformation, changes in light reflection and other low level images featured are combined in a Bayesian framework to achieve an overall frame-by-frame classification accuracy of 77% and episode classification accuracy of 85%.</div>
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<Author AffiliationIDS="Aff6"><AuthorName DisplayOrder="Western"><GivenName>Guang-Zhong</GivenName>
<FamilyName>Yang</FamilyName>
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<Affiliation ID="Aff6"><OrgDivision>Royal Society/Wolfson Medical Image Computing Laboratory</OrgDivision>
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<Para>The assessment of surgical skills for Minimally Invasive Surgery (MIS) has traditionally been conducted with visual observation and objective scoring. This paper presents a practical framework for the detection of instrument/tissue interaction from MIS video sequences by incorporating multiple visual cues. The proposed technique investigates the characteristics of four major events involved in MIS procedures including idle, retraction, cauterisation and suturing. Constant instrument tracking is maintained and multiple visual cues related to shape, deformation, changes in light reflection and other low level images featured are combined in a Bayesian framework to achieve an overall frame-by-frame classification accuracy of 77% and episode classification accuracy of 85%.</Para>
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<mods version="3.6"><titleInfo lang="en"><title>Episode Classification for the Analysis of Tissue/Instrument Interaction with Multiple Visual Cues</title>
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<namePart type="given">L.</namePart>
<namePart type="family">Lo</namePart>
<affiliation>Royal Society/Wolfson Medical Image Computing Laboratory, Imperial College London, London, United Kingdom</affiliation>
<affiliation>E-mail: benny.lo@imperial.ac.uk</affiliation>
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<name type="personal"><namePart type="given">Ara</namePart>
<namePart type="family">Darzi</namePart>
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<affiliation>E-mail: a.darzi@imperial.ac.uk</affiliation>
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<name type="personal"><namePart type="given">Guang-Zhong</namePart>
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<affiliation>Royal Society/Wolfson Medical Image Computing Laboratory, Imperial College London, London, United Kingdom</affiliation>
<affiliation>E-mail: g.z.yang@imperial.ac.uk</affiliation>
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<abstract lang="en">Abstract: The assessment of surgical skills for Minimally Invasive Surgery (MIS) has traditionally been conducted with visual observation and objective scoring. This paper presents a practical framework for the detection of instrument/tissue interaction from MIS video sequences by incorporating multiple visual cues. The proposed technique investigates the characteristics of four major events involved in MIS procedures including idle, retraction, cauterisation and suturing. Constant instrument tracking is maintained and multiple visual cues related to shape, deformation, changes in light reflection and other low level images featured are combined in a Bayesian framework to achieve an overall frame-by-frame classification accuracy of 77% and episode classification accuracy of 85%.</abstract>
<relatedItem type="host"><titleInfo><title>Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003</title>
<subTitle>6th International Conference, Montréal, Canada, November 15-18, 2003. Proceedings</subTitle>
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<name type="personal"><namePart type="given">Randy</namePart>
<namePart type="given">E.</namePart>
<namePart type="family">Ellis</namePart>
<affiliation>Queen’s University, Kingston, Ontario, Canada</affiliation>
<affiliation>E-mail: ellis@cs.queensu.ca</affiliation>
<role><roleTerm type="text">editor</roleTerm>
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<name type="personal"><namePart type="given">Terry</namePart>
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<topic authority="SpringerSubjectCodes" authorityURI="I22021">Image Processing and Computer Vision</topic>
<topic authority="SpringerSubjectCodes" authorityURI="H28009">Health Informatics</topic>
<topic authority="SpringerSubjectCodes" authorityURI="H29005">Imaging / Radiology</topic>
<topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic>
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<identifier type="DOI">10.1007/b93810</identifier>
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<detail type="part"><title>Robotic Mechanism ans Mechanical Properties of Tissue</title>
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<namePart type="family">Goos</namePart>
<affiliation>Karlsruhe University, Germany</affiliation>
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<name type="personal"><namePart type="given">Juris</namePart>
<namePart type="family">Hartmanis</namePart>
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<name type="personal"><namePart type="given">Jan</namePart>
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