Publikation:

Synthetic data generation for optical flow evaluation in the neurosurgical domain

Lade...
Vorschaubild

Dateien

Philipp_2-at57mr73fomd8.pdf
Philipp_2-at57mr73fomd8.pdfGröße: 2.08 MBDownloads: 412

Datum

2021

Autor:innen

Philipp, Markus
Bacher, Neal
Nienhaus, Jonas
Hauptmann, Lars
Lang, Laura
Gutt-Will, Marielena
Mathis, Andrea
Saur, Stefan
Raabe, Andreas

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Link zur Lizenz

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Gold
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

Current Directions in Biomedical Engineering. De Gruyter. 2021, 7(1), pp. 67-71. eISSN 2364-5504. Available under: doi: 10.1515/cdbme-2021-1015

Zusammenfassung

Towards computer-assisted neurosurgery, scene understanding algorithms for microscope video data are required. Previous work utilizes optical flow to extract spatiotemporal context from neurosurgical video sequences. However, to select an appropriate optical flow method, we need to analyze which algorithm yields the highest accuracy for the neurosurgical domain. Currently, there are no benchmark datasets available for neurosurgery. In our work, we present an approach to generate synthetic data for optical flow evaluation on the neurosurgical domain. We simulate image sequences and thereby take into account domainspecific visual conditions such as surgical instrument motion. Then, we evaluate two optical flow algorithms, Farneback and PWC-Net, on our synthetic data. Qualitative and quantitative assessments confirm that our data can be used to evaluate optical flow for the neurosurgical domain. Future work will concentrate on extending the method by modeling additional effects in neurosurgery such as elastic background motion.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Neurosurgery, surgical microscope, optical flow, evaluation

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690PHILIPP, Markus, Neal BACHER, Jonas NIENHAUS, Lars HAUPTMANN, Laura LANG, Anna ALPEROVICH, Marielena GUTT-WILL, Andrea MATHIS, Stefan SAUR, Andreas RAABE, Franziska MATHIS-ULLRICH, 2021. Synthetic data generation for optical flow evaluation in the neurosurgical domain. In: Current Directions in Biomedical Engineering. De Gruyter. 2021, 7(1), pp. 67-71. eISSN 2364-5504. Available under: doi: 10.1515/cdbme-2021-1015
BibTex
@article{Philipp2021-08-27Synth-56384,
  year={2021},
  doi={10.1515/cdbme-2021-1015},
  title={Synthetic data generation for optical flow evaluation in the neurosurgical domain},
  number={1},
  volume={7},
  journal={Current Directions in Biomedical Engineering},
  pages={67--71},
  author={Philipp, Markus and Bacher, Neal and Nienhaus, Jonas and Hauptmann, Lars and Lang, Laura and Alperovich, Anna and Gutt-Will, Marielena and Mathis, Andrea and Saur, Stefan and Raabe, Andreas and Mathis-Ullrich, Franziska}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/56384">
    <dc:contributor>Bacher, Neal</dc:contributor>
    <dc:contributor>Gutt-Will, Marielena</dc:contributor>
    <dc:contributor>Lang, Laura</dc:contributor>
    <dcterms:issued>2021-08-27</dcterms:issued>
    <dc:creator>Mathis-Ullrich, Franziska</dc:creator>
    <dc:creator>Mathis, Andrea</dc:creator>
    <dc:language>eng</dc:language>
    <dc:creator>Nienhaus, Jonas</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/56384"/>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dc:contributor>Mathis, Andrea</dc:contributor>
    <dc:creator>Saur, Stefan</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-01-28T09:38:35Z</dc:date>
    <dc:creator>Lang, Laura</dc:creator>
    <dc:contributor>Raabe, Andreas</dc:contributor>
    <dc:creator>Alperovich, Anna</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/56384/3/Philipp_2-at57mr73fomd8.pdf"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Philipp, Markus</dc:contributor>
    <dc:creator>Bacher, Neal</dc:creator>
    <dc:creator>Gutt-Will, Marielena</dc:creator>
    <dc:creator>Raabe, Andreas</dc:creator>
    <dc:contributor>Saur, Stefan</dc:contributor>
    <dcterms:title>Synthetic data generation for optical flow evaluation in the neurosurgical domain</dcterms:title>
    <dc:contributor>Nienhaus, Jonas</dc:contributor>
    <dc:contributor>Hauptmann, Lars</dc:contributor>
    <dc:contributor>Mathis-Ullrich, Franziska</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-01-28T09:38:35Z</dcterms:available>
    <dcterms:abstract xml:lang="eng">Towards computer-assisted neurosurgery, scene understanding algorithms for microscope video data are required. Previous work utilizes optical flow to extract spatiotemporal context from neurosurgical video sequences. However, to select an appropriate optical flow method, we need to analyze which algorithm yields the highest accuracy for the neurosurgical domain. Currently, there are no benchmark datasets available for neurosurgery. In our work, we present an approach to generate synthetic data for optical flow evaluation on the neurosurgical domain. We simulate image sequences and thereby take into account domainspecific visual conditions such as surgical instrument motion. Then, we evaluate two optical flow algorithms, Farneback and PWC-Net, on our synthetic data. Qualitative and quantitative assessments confirm that our data can be used to evaluate optical flow for the neurosurgical domain. Future work will concentrate on extending the method by modeling additional effects in neurosurgery such as elastic background motion.</dcterms:abstract>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/56384/3/Philipp_2-at57mr73fomd8.pdf"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Philipp, Markus</dc:creator>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
    <dc:creator>Hauptmann, Lars</dc:creator>
    <dc:contributor>Alperovich, Anna</dc:contributor>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Ja
Diese Publikation teilen