Superpixel-based structure classification for laparoscopic surgery

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BODENSTEDT, Sebastian, Jochen GÖRTLER, Martin WAGNER, Hannes KENNGOTT, Beat Peter MÜLLER-STICH, Rüdiger DILLMANN, Stefanie SPEIDEL, 2016. Superpixel-based structure classification for laparoscopic surgery. Medical Imaging 2016. San Diego, 28. Feb 2016 - 1. Mrz 2016. In: WEBSTER, Robert J., ed., Ziv R. YANIV, ed.. Medical Imaging 2016 : Image-Guided Procedures, Robotic Interventions, and Modeling. Medical Imaging 2016. San Diego, 28. Feb 2016 - 1. Mrz 2016. Bellingham:SPIE, pp. ´. ISSN 1605-7422. eISSN 2410-9045. ISBN 978-1-5106-0021-8

@inproceedings{Bodenstedt2016Super-36996, title={Superpixel-based structure classification for laparoscopic surgery}, year={2016}, doi={10.1117/12.2216750}, number={17}, isbn={978-1-5106-0021-8}, issn={1605-7422}, address={Bellingham}, publisher={SPIE}, series={Progress in Biomedical Optics and Imaging}, booktitle={Medical Imaging 2016 : Image-Guided Procedures, Robotic Interventions, and Modeling}, editor={Webster, Robert J. and Yaniv, Ziv R.}, author={Bodenstedt, Sebastian and Görtler, Jochen and Wagner, Martin and Kenngott, Hannes and Müller-Stich, Beat Peter and Dillmann, Rüdiger and Speidel, Stefanie}, note={Article Number: 978618} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/36996"> <dc:contributor>Müller-Stich, Beat Peter</dc:contributor> <dcterms:title>Superpixel-based structure classification for laparoscopic surgery</dcterms:title> <dc:creator>Kenngott, Hannes</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-01-31T11:40:19Z</dcterms:available> <dc:creator>Speidel, Stefanie</dc:creator> <dc:creator>Müller-Stich, Beat Peter</dc:creator> <dc:creator>Görtler, Jochen</dc:creator> <dc:contributor>Dillmann, Rüdiger</dc:contributor> <dc:contributor>Bodenstedt, Sebastian</dc:contributor> <dc:creator>Dillmann, Rüdiger</dc:creator> <dc:language>eng</dc:language> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-01-31T11:40:19Z</dc:date> <dc:contributor>Görtler, Jochen</dc:contributor> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/36996"/> <dc:creator>Wagner, Martin</dc:creator> <dcterms:abstract xml:lang="eng">Minimally-invasive interventions offers multiple benefits for patients, but also entails drawbacks for the surgeon. The goal of context-aware assistance systems is to alleviate some of these difficulties. Localizing and identifying anatomical structures, maligned tissue and surgical instruments through endoscopic image analysis is paramount for an assistance system, making online measurements and augmented reality visualizations possible. Furthermore, such information can be used to assess the progress of an intervention, hereby allowing for a context-aware assistance. In this work, we present an approach for such an analysis. First, a given laparoscopic image is divided into groups of connected pixels, so-called superpixels, using the SEEDS algorithm. The content of a given superpixel is then described using information regarding its color and texture. Using a Random Forest classifier, we determine the class label of each superpixel. We evaluated our approach on a publicly available dataset for laparoscopic instrument detection and achieved a DICE score of 0.69.</dcterms:abstract> <dc:contributor>Wagner, Martin</dc:contributor> <dc:contributor>Speidel, Stefanie</dc:contributor> <dc:contributor>Kenngott, Hannes</dc:contributor> <dcterms:rights rdf:resource="http://nbn-resolving.de/urn:nbn:de:bsz:352-20150914100631302-4485392-8"/> <dcterms:issued>2016</dcterms:issued> <dc:creator>Bodenstedt, Sebastian</dc:creator> </rdf:Description> </rdf:RDF>

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