Stammdaten

Titel: Identifying Surgical Instruments in Laparoscopy Using Deep Learning Instance Segmentation
Untertitel:
Kurzfassung:

Recorded videos from surgeries have become an increasingly important information source for the field of medical endoscopy, since the recorded footage shows every single detail of the surgery. However, while video recording is straightforward these days, automatic content indexing - the basis for content-based search in a medical video archive - is still a great challenge due to the very special video content. In this work, we investigate segmentation and recognition of surgical instruments in videos recorded from laparoscopic gynecology. More precisely, we evaluate the achievable performance of segmenting surgical instruments from their background by using a region-based fully convolutional network for instance-aware (1) instrument segmentation as well as (2) instrument recognition. While the first part addresses only binary segmentation of instances (i.e., distinguishing between instrument or background) we also investigate multi-class instrument recognition (i.e., identifying the type of instrument). Our evaluation results show that even with a moderately low number of training examples, we are able to localize and segment instrument regions with a pretty high accuracy. However, the results also reveal that determining the particular instrument is still very challenging, due to the inherently high similarity of surgical instruments.

Schlagworte: Instruments, Surgery, Laparoscopes, Image segmentation, Videos, Task analysis, Robots
Publikationstyp: Beitrag in Proceedings (Autorenschaft)
Art der Veröffentlichung Online Publikation
Erschienen in: Proceedings of the International Conference on Content-Based Multimedia Indexing (CBMI'19)
Proceedings of the International Conference on Content-Based Multimedia Indexing (CBMI'19)
zur Publikation
 ( IEEE; )
Erscheinungsdatum: 21.10.2019
Titel der Serie: -
Bandnummer: -
Erstveröffentlichung: Ja
Version: -
Seite: -

Identifikatoren

ISBN:
  • 978-1-7281-4673-7
ISSN: -
DOI: http://dx.doi.org/10.1109/CBMI.2019.8877379
AC-Nummer: -
Homepage: https://ieeexplore.ieee.org/document/8877379
Open Access
  • Online verfügbar (nicht Open Access)

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020  Klagenfurt am Wörthersee
Österreich
  -3699
   martina.steinbacher@uni-klu.ac.at
http://itec.aau.at/
zur Organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 202022 - Informationstechnik
Forschungscluster Kein Forschungscluster ausgewählt
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: II)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Distributed Multimedia Systems

Kooperationen

Organisation Adresse
UNIVERSITE DE BORDEAUX 1 / LABRI lab
351, COURS DE LA LIBERATION
33405  TALANCE
Frankreich
351, COURS DE LA LIBERATION
FR - 33405  TALANCE
Medizinische Universität Wien
Spitalgasse 23
1090  Wien
Österreich
Spitalgasse 23
AT - 1090  Wien

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