Workflow Phase Detection in Fluoroscopic Images Using Convolutional Neural Networks

Arbogast N, Kurzendorfer T, Breininger K, Mountney P, Toth D, Narayan SA, Maier A (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer Berlin Heidelberg

Pages Range: 191-196

Conference Proceedings Title: Informatik aktuell

Event location: Lübeck DE

ISBN: 9783658253257

DOI: 10.1007/978-3-658-25326-4_41

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.

Authors with CRIS profile

Tanja Kurzendorfer Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Katharina Breininger Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

King’s College London GB United Kingdom (GB) Siemens AG, Healthcare Sector DE Germany (DE)

How to cite

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.

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