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
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ISTEX:8BCC7FC2D643FD3FC02B81393670B76F301B3671Le document en format XML
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Lo</name><affiliation><mods:affiliation>Royal Society/Wolfson Medical Image Computing Laboratory, Imperial College London, London, United Kingdom</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: benny.lo@imperial.ac.uk</mods:affiliation></affiliation></author><author><name sortKey="Darzi, Ara" sort="Darzi, Ara" uniqKey="Darzi A" first="Ara" last="Darzi">Ara Darzi</name><affiliation><mods:affiliation>Royal Society/Wolfson Medical Image Computing Laboratory, Imperial College London, London, United Kingdom</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: a.darzi@imperial.ac.uk</mods:affiliation></affiliation></author><author><name sortKey="Yang, Guang Zhong" sort="Yang, Guang Zhong" uniqKey="Yang G" first="Guang-Zhong" last="Yang">Guang-Zhong Yang</name><affiliation><mods:affiliation>Royal Society/Wolfson Medical Image Computing Laboratory, Imperial College London, London, United Kingdom</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: g.z.yang@imperial.ac.uk</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:8BCC7FC2D643FD3FC02B81393670B76F301B3671</idno><date when="2003" year="2003">2003</date><idno type="doi">10.1007/978-3-540-39899-8_29</idno><idno type="url">https://api.istex.fr/document/8BCC7FC2D643FD3FC02B81393670B76F301B3671/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">000A45</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Episode Classification for the Analysis of Tissue/Instrument Interaction with Multiple Visual Cues</title><author><name sortKey="Lo, L" sort="Lo, L" uniqKey="Lo L" first="L." last="Lo">L. Lo</name><affiliation><mods:affiliation>Royal Society/Wolfson Medical Image Computing Laboratory, Imperial College London, London, United Kingdom</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: benny.lo@imperial.ac.uk</mods:affiliation></affiliation></author><author><name sortKey="Darzi, Ara" sort="Darzi, Ara" uniqKey="Darzi A" first="Ara" last="Darzi">Ara Darzi</name><affiliation><mods:affiliation>Royal Society/Wolfson Medical Image Computing Laboratory, Imperial College London, London, United Kingdom</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: a.darzi@imperial.ac.uk</mods:affiliation></affiliation></author><author><name sortKey="Yang, Guang Zhong" sort="Yang, Guang Zhong" uniqKey="Yang G" first="Guang-Zhong" last="Yang">Guang-Zhong Yang</name><affiliation><mods:affiliation>Royal Society/Wolfson Medical Image Computing Laboratory, Imperial College London, London, United Kingdom</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: g.z.yang@imperial.ac.uk</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2003</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">8BCC7FC2D643FD3FC02B81393670B76F301B3671</idno><idno type="DOI">10.1007/978-3-540-39899-8_29</idno><idno type="ChapterID">29</idno><idno type="ChapterID">Chap29</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><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></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Benny P. L. 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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></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Episode Classification for the Analysis of Tissue/Instrument Interaction with Multiple Visual Cues</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Episode Classification for the Analysis of Tissue/Instrument Interaction with Multiple Visual Cues</title></titleInfo><name type="personal"><namePart type="given">Benny</namePart><namePart type="given">P.</namePart><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><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ara</namePart><namePart type="family">Darzi</namePart><affiliation>Royal Society/Wolfson Medical Image Computing Laboratory, Imperial College London, London, United Kingdom</affiliation><affiliation>E-mail: a.darzi@imperial.ac.uk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Guang-Zhong</namePart><namePart type="family">Yang</namePart><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><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2003</dateIssued><copyrightDate encoding="w3cdtf">2003</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><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></titleInfo><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></role></name><name type="personal"><namePart type="given">Terry</namePart><namePart type="given">M.</namePart><namePart type="family">Peters</namePart><affiliation>Canadian Surgical Technologies and Advanced Robotics, London, Ontario, Canada</affiliation><affiliation>E-mail: tpeters@imaging.robarts.ca</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2003</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><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><topic authority="SpringerSubjectCodes" authorityURI="I22013">Computer Graphics</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic></subject><identifier type="DOI">10.1007/b93810</identifier><identifier type="ISBN">978-3-540-20462-6</identifier><identifier type="eISBN">978-3-540-39899-8</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">80281</identifier><identifier type="BookID">978-3-540-39899-8</identifier><identifier type="BookChapterCount">98</identifier><identifier type="BookVolumeNumber">2878</identifier><identifier type="BookSequenceNumber">2878</identifier><identifier type="PartChapterCount">20</identifier><part><date>2003</date><detail type="part"><title>Robotic Mechanism ans Mechanical Properties of Tissue</title></detail><detail type="volume"><number>2878</number><caption>vol.</caption></detail><extent unit="pages"><start>230</start><end>237</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2003</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Goos</namePart><affiliation>Karlsruhe University, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Juris</namePart><namePart type="family">Hartmanis</namePart><affiliation>Cornell University, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jan</namePart><namePart type="family">van Leeuwen</namePart><affiliation>Utrecht University, The Netherlands</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><copyrightDate 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