Titel: Frame-Based Classification of Operation Phases in Cataract Surgery Videos

Cataract surgeries are frequently performed to correct a lens opacification of the human eye, which usually appears in the course of aging. These surgeries are conducted with the help of a microscope and are typically recorded on video for later inspection and educational purposes. However, post-hoc visual analysis of video recordings is cumbersome and time-consuming for surgeons if there is no navigation support, such as bookmarks to specific operation phases. To prepare the way for an automatic detection of operation phases in cataract surgery videos, we investigate the effectiveness of a deep convolutional neural network (CNN) to automatically assign video frames to operation phases, which can be regarded as a single-label multi-class classification problem. In absence of public datasets of cataract surgery videos, we provide a dataset of 21 videos of standardized cataract surgeries and use it to train and evaluate our CNN classifier. Experimental results display a mean F1-score of about 68% for frame-based operation phase classification, which can be further improved to 75% when considering temporal information of video frames in the CNN architecture.

Publikationstyp: Beitrag in Sammelwerk (Autorenschaft)
Art der Veröffentlichung Printversion
Erschienen in: MultiMedia Modeling - 24th International Conference, MMM 2018 (Part 1)
MultiMedia Modeling - 24th International Conference, MMM 2018 (Part 1) (2018)

K. Schöffmann, T. Chalidabhongse, C. Ngo, N. O´Connor, S. Aramvith, Y. Ho, M. Gabbouj, A. Elgammal

zur Publikation
 ( Springer; K. Schöffmann, T. Chalidabhongse, C. Ngo, N. O´Connor, S. Aramvith, Y. Ho, M. Gabbouj, A. Elgammal )
Erscheinungsdatum: 01.2018
Titel der Serie: LNCS
Bandnummer: 10704
Erstveröffentlichung: Ja
Seite: S. 241 - 253
Bild der Titelseite: Cover


  • 978-3-319-73602-0
ISSN: 0302-9743
AC-Nummer: -
Open Access
  • Online verfügbar (nicht Open Access)


Organisation Adresse
Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020 Klagenfurt am Wörthersee
zur Organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee


  • 102020 - Medizinische Informatik (305905)
Forschungscluster Kein Forschungscluster ausgewählt
Peer Reviewed
  • Ja
  • Science to Science (Qualitätsindikator: II)
Klassifikationsraster der zugeordneten Organisationseinheiten:
  • Distributed Multimedia Systems


Organisation Adresse
KABEG Klinikum Klagenfurt
Feschnigstraße 11
9020 Klagenfurt
Feschnigstraße 11
AT - 9020  Klagenfurt

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