Stammdaten

Titel: Pixel-Based Tool Segmentation in Cataract Surgery Videos with Mask R-CNN
Untertitel:
Kurzfassung:

Automatically detecting surgical tools in recorded surgery videos is an important building block of further content-based video analysis. In ophthalmology, the results of such methods can support training and teaching of operation techniques and enable investigation of medical research questions on a dataset of recorded surgery videos. While previous methods used frame-based classification techniques to predict the presence of surgical tools — but did not localize them, we apply a recent deep-learning segmentation method (Mask R-CNN) to localize and segment surgical tools used in ophthalmic cataract surgery. We add ground-truth annotations for multi-class instance segmentation to two existing datasets of cataract surgery videos and make resulting datasets publicly available for research purposes. In the absence of comparable results from literature, we tune and evaluate the Mask R-CNN approach on these datasets for instrument segmentation/localization and achieve promising results (61\% mean average precision on 50\% intersection over union for instance segmentation, working even better for bounding box detection or binary segmentation), establishing a reasonable baseline for further research. Moreover, we experiment with common data augmentation techniques and analyze the achieved segmentation performance with respect to each class (instrument), providing evidence for future improvements of this approach.

Schlagworte: cataract surgeries, instrument segmentation, tool annotation, deep neural networks, ophthalmology
Publikationstyp: Beitrag in Proceedings (Autorenschaft)
Erscheinungsdatum: 01.09.2020 (Online)
Erschienen in: Proceedings of the 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)
Proceedings of the 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)
zur Publikation
 ( IEEE; )
Titel der Serie: -
Bandnummer: -
Erstveröffentlichung: Ja
Version: -
Seite: S. 565 - 568

Versionen

Keine Version vorhanden
Erscheinungsdatum: 01.09.2020
ISBN (e-book):
  • 978-1-7281-9429-5
  • 978-1-7281-9430-1
eISSN: 2372-918X
DOI: http://dx.doi.org/10.1109/CBMS49503.2020.00112
Homepage: https://ieeexplore.ieee.org/document/9183116
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
   martina.steinbacher@aau.at
http://itec.aau.at/
zur Organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 1020 - Informatik
Forschungscluster Kein Forschungscluster ausgewählt
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: II)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Distributed Multimedia Systems

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