Workflow Phase Detection in Fluoroscopic Images Using Convolutional Neural Networks

Beitrag bei einer Tagung


Details zur Publikation

Autorinnen und Autoren: Arbogast N, Kurzendorfer T, Breininger K, Mountney P, Toth D, Narayan SA, Maier A
Herausgeber: Thomas M. Deserno, Andreas Maier, Christoph Palm, Heinz Handels, Klaus H. Maier-Hein, Thomas Tolxdorff
Verlag: Springer Berlin Heidelberg
Jahr der Veröffentlichung: 2019
Tagungsband: Informatik aktuell
Seitenbereich: 191-196
ISBN: 9783658253257
ISSN: 1431-472X


Abstract

In image guided interventions, the radiation dose to the patient and personnel can be reduced by positioning the blades of a collimator to block off unnecessary X-rays and restrict the irradiated area to a region of interest. In a certain stage of the operation workflow phase detection can define objects of interest to enable automatic collimation. Workflow phase detection can be beneficial for clinical time management or operating rooms of the future. In this work, we propose a learningbased approach for an automatic classification of three surgical workflow phases. Our data consists of 24 congenital cardiac interventions with a total of 2985 fluoroscopic 2D X-ray images. We compare two different convolutional neural network architectures and investigate their performance regarding each phase. Using a residual network, a class-wise averaged accuracy of 86.14% was achieved. The predictions of the trained models can then be used for context specific collimation.


FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Breininger, Katharina
Lehrstuhl für Informatik 5 (Mustererkennung)
Kurzendorfer, Tanja
Lehrstuhl für Informatik 5 (Mustererkennung)
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)


Einrichtungen weiterer Autorinnen und Autoren

King’s College London
Siemens AG, Healthcare Sector


Zitierweisen

APA:
Arbogast, N., Kurzendorfer, T., Breininger, K., Mountney, P., Toth, D., Narayan, S.A., & Maier, A. (2019). Workflow Phase Detection in Fluoroscopic Images Using Convolutional Neural Networks. In Thomas M. Deserno, Andreas Maier, Christoph Palm, Heinz Handels, Klaus H. Maier-Hein, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 191-196). Lübeck, DE: Springer Berlin Heidelberg.

MLA:
Arbogast, Nikolaus, et al. "Workflow Phase Detection in Fluoroscopic Images Using Convolutional Neural Networks." Proceedings of the Workshop on Bildverarbeitung fur die Medizin, 2019, Lübeck Ed. Thomas M. Deserno, Andreas Maier, Christoph Palm, Heinz Handels, Klaus H. Maier-Hein, Thomas Tolxdorff, Springer Berlin Heidelberg, 2019. 191-196.

BibTeX: 

Zuletzt aktualisiert 2019-15-05 um 10:23