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Multiple Device Segmentation for Fluoroscopic Imaging Using Multi-task Learning

Breininger K, Würfl T, Kurzendorfer T, Albarqouni S, Pfister M, Kowarschik M, Navab N, Maier A (2018)

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Publication Type: Conference contribution

Publication year: 2018

Publisher: Springer International Publishing

Edited Volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

City/Town: Springer

Book Volume: 11043 LNCS

Pages Range: 19-27

Conference Proceedings Title: Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis

Event location: Granada, Spain

ISBN: 9783030013639

DOI: 10.1007/978-3-030-01364-6_3

Abstract

For endovascular aortic repair (EVAR), integrating preoperative information of the aortic anatomy with intraoperative fluoroscopy can aid in reducing radiation exposure, contrast agent and procedure time. However, the quality of this fusion may deteriorate over the course of the intervention due to patient movement or deformation of the vasculature caused by interventional tools. Automatically detecting the instruments present in the X-ray image can help to assess the degree of deterioration, trigger automatic re-registration or aid in automatic workflow phase detection and process modeling. In this work, we investigate a flexible approach to segment different devices based on fully convolutional neural networks using multi-task learning. We evaluate the proposed approach on a set of 38 X-ray images acquired during EVAR interventions by targeting the segmentation of aortic stents, stiff guidewires and pigtail catheters. We compare the results to the performance of single-task networks. We manage to keep similar performance compared to single-task networks with Dice coefficients between 0.95 and 0.80 depending on the device, while speeding up computation by a factor of two.

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How to cite

APA:

Breininger, K., Würfl, T., Kurzendorfer, T., Albarqouni, S., Pfister, M., Kowarschik, M.,... Maier, A. (2018). Multiple Device Segmentation for Fluoroscopic Imaging Using Multi-task Learning. In Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (pp. 19-27). Granada, Spain: Springer: Springer International Publishing.

MLA:

Breininger, Katharina, et al. "Multiple Device Segmentation for Fluoroscopic Imaging Using Multi-task Learning." Proceedings of the Joint MICCAI-Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT), Granada, Spain Springer: Springer International Publishing, 2018. 19-27.

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