Modeling and Online Recognition of Surgical Phases Using Hidden Markov Models
Identifieur interne : 004357 ( Main/Exploration ); précédent : 004356; suivant : 004358Modeling and Online Recognition of Surgical Phases Using Hidden Markov Models
Auteurs : Tobias Blum [Allemagne] ; Nicolas Padoy [Allemagne, France] ; Hubertus Feu Ner [Allemagne] ; Nassir Navab [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: The amount of signals that can be recorded during a surgery, like tracking data or state of instruments, is constantly growing. These signals can be used to better understand surgical workflow and to build surgical assist systems that are aware of the current state of a surgery. This is a crucial issue for designing future systems that provide context-sensitive information and user interfaces. In this paper, Hidden Markov Models (HMM) are used to model a laparoscopic cholecystectomy. Seventeen signals, representing tool usage, from twelve surgeries are used to train the model. The use of a model merging approach is proposed to build the HMM topology and compared to other methods of initializing a HMM. The merging method allows building a model at a very fine level of detail that also reveals the workflow of a surgery in a human-understandable way. Results for detecting the current phase of a surgery and for predicting the remaining time of the procedure are presented.
Url:
DOI: 10.1007/978-3-540-85990-1_75
Affiliations:
- Allemagne, France
- Bavière, District de Haute-Bavière, Grand Est, Lorraine (région)
- Munich, Nancy
- Université technique de Munich
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 001E62
- to stream Istex, to step Curation: 001E38
- to stream Istex, to step Checkpoint: 000D85
- to stream Main, to step Merge: 004468
- to stream Main, to step Curation: 004357
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Modeling and Online Recognition of Surgical Phases Using Hidden Markov Models</title><author><name sortKey="Blum, Tobias" sort="Blum, Tobias" uniqKey="Blum T" first="Tobias" last="Blum">Tobias Blum</name></author><author><name sortKey="Padoy, Nicolas" sort="Padoy, Nicolas" uniqKey="Padoy N" first="Nicolas" last="Padoy">Nicolas Padoy</name></author><author><name sortKey="Feu Ner, Hubertus" sort="Feu Ner, Hubertus" uniqKey="Feu Ner H" first="Hubertus" last="Feu Ner">Hubertus Feu Ner</name></author><author><name sortKey="Navab, Nassir" sort="Navab, Nassir" uniqKey="Navab N" first="Nassir" last="Navab">Nassir Navab</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:84381AF0E6D22ACB0D53C9668B2DEC3118A4E0DA</idno><date when="2008" year="2008">2008</date><idno type="doi">10.1007/978-3-540-85990-1_75</idno><idno type="url">https://api.istex.fr/ark:/67375/HCB-H9X97NMK-8/fulltext.pdf</idno><idno type="wicri:Area/Istex/Corpus">001E62</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001E62</idno><idno type="wicri:Area/Istex/Curation">001E38</idno><idno type="wicri:Area/Istex/Checkpoint">000D85</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Checkpoint">000D85</idno><idno type="wicri:doubleKey">0302-9743:2008:Blum T:modeling:and:online</idno><idno type="wicri:Area/Main/Merge">004468</idno><idno type="wicri:Area/Main/Curation">004357</idno><idno type="wicri:Area/Main/Exploration">004357</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Modeling and Online Recognition of Surgical Phases Using Hidden Markov Models</title><author><name sortKey="Blum, Tobias" sort="Blum, Tobias" uniqKey="Blum T" first="Tobias" last="Blum">Tobias Blum</name><affiliation wicri:level="4"><country xml:lang="fr">Allemagne</country><wicri:regionArea>Computer Aided Medical Procedures (CAMP), Technische Universität München</wicri:regionArea><placeName><settlement type="city">Munich</settlement><region type="land" nuts="1">Bavière</region><region type="district" nuts="2">District de Haute-Bavière</region></placeName><orgName type="university">Université technique de Munich</orgName></affiliation></author><author><name sortKey="Padoy, Nicolas" sort="Padoy, Nicolas" uniqKey="Padoy N" first="Nicolas" last="Padoy">Nicolas Padoy</name><affiliation wicri:level="4"><country xml:lang="fr">Allemagne</country><wicri:regionArea>Computer Aided Medical Procedures (CAMP), Technische Universität München</wicri:regionArea><placeName><settlement type="city">Munich</settlement><region type="land" nuts="1">Bavière</region><region type="district" nuts="2">District de Haute-Bavière</region></placeName><orgName type="university">Université technique de Munich</orgName></affiliation><affiliation wicri:level="3"><country xml:lang="fr">France</country><wicri:regionArea>LORIA-INRIA Lorraine, Nancy</wicri:regionArea><placeName><region type="region">Grand Est</region><region type="old region">Lorraine (région)</region><settlement type="city">Nancy</settlement></placeName></affiliation></author><author><name sortKey="Feu Ner, Hubertus" sort="Feu Ner, Hubertus" uniqKey="Feu Ner H" first="Hubertus" last="Feu Ner">Hubertus Feu Ner</name><affiliation wicri:level="4"><country xml:lang="fr">Allemagne</country><wicri:regionArea>Department of Surgery, Klinikum Rechts der Isar, Technische Universität München</wicri:regionArea><placeName><settlement type="city">Munich</settlement><region type="land" nuts="1">Bavière</region><region type="district" nuts="2">District de Haute-Bavière</region></placeName><orgName type="university">Université technique de Munich</orgName></affiliation></author><author><name sortKey="Navab, Nassir" sort="Navab, Nassir" uniqKey="Navab N" first="Nassir" last="Navab">Nassir Navab</name><affiliation wicri:level="4"><country xml:lang="fr">Allemagne</country><wicri:regionArea>Computer Aided Medical Procedures (CAMP), Technische Universität München</wicri:regionArea><placeName><settlement type="city">Munich</settlement><region type="land" nuts="1">Bavière</region><region type="district" nuts="2">District de Haute-Bavière</region></placeName><orgName type="university">Université technique de Munich</orgName></affiliation></author></analytic><monogr></monogr><series><title level="s" type="main" xml:lang="en">Lecture Notes in Computer Science</title><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: The amount of signals that can be recorded during a surgery, like tracking data or state of instruments, is constantly growing. These signals can be used to better understand surgical workflow and to build surgical assist systems that are aware of the current state of a surgery. This is a crucial issue for designing future systems that provide context-sensitive information and user interfaces. In this paper, Hidden Markov Models (HMM) are used to model a laparoscopic cholecystectomy. Seventeen signals, representing tool usage, from twelve surgeries are used to train the model. The use of a model merging approach is proposed to build the HMM topology and compared to other methods of initializing a HMM. The merging method allows building a model at a very fine level of detail that also reveals the workflow of a surgery in a human-understandable way. Results for detecting the current phase of a surgery and for predicting the remaining time of the procedure are presented.</div></front></TEI><affiliations><list><country><li>Allemagne</li><li>France</li></country><region><li>Bavière</li><li>District de Haute-Bavière</li><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Munich</li><li>Nancy</li></settlement><orgName><li>Université technique de Munich</li></orgName></list><tree><country name="Allemagne"><region name="Bavière"><name sortKey="Blum, Tobias" sort="Blum, Tobias" uniqKey="Blum T" first="Tobias" last="Blum">Tobias Blum</name></region><name sortKey="Feu Ner, Hubertus" sort="Feu Ner, Hubertus" uniqKey="Feu Ner H" first="Hubertus" last="Feu Ner">Hubertus Feu Ner</name><name sortKey="Navab, Nassir" sort="Navab, Nassir" uniqKey="Navab N" first="Nassir" last="Navab">Nassir Navab</name><name sortKey="Padoy, Nicolas" sort="Padoy, Nicolas" uniqKey="Padoy N" first="Nicolas" last="Padoy">Nicolas Padoy</name></country><country name="France"><region name="Grand Est"><name sortKey="Padoy, Nicolas" sort="Padoy, Nicolas" uniqKey="Padoy N" first="Nicolas" last="Padoy">Nicolas Padoy</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 004357 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 004357 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Lorraine
|area= InforLorV4
|flux= Main
|étape= Exploration
|type= RBID
|clé= ISTEX:84381AF0E6D22ACB0D53C9668B2DEC3118A4E0DA
|texte= Modeling and Online Recognition of Surgical Phases Using Hidden Markov Models
}}
| This area was generated with Dilib version V0.6.33. |  |