AutoDVT: Joint real-time classification for vein compressibility analysis in deep vein thrombosis ultrasound diagnostics
Tanno R, Makropoulos A, Arslan S, Oktay O, Mischkewitz S, Al-Noor F, Oppenheimer J, Mandegaran R, Kainz B, Heinrich MP (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11071 LNCS
Pages Range: 905-912
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030009335
DOI: 10.1007/978-3-030-00934-2_100
Abstract
We propose a dual-task convolutional neural network (CNN) to fully automate the real-time diagnosis of deep vein thrombosis (DVT). DVT can be reliably diagnosed through evaluation of vascular compressibility at anatomically defined landmarks in streams of ultrasound (US) images. The combined real-time evaluation of these tasks has never been achieved before. As proof-of-concept, we evaluate our approach on two selected landmarks of the femoral vein, which can be identified with high accuracy by our approach. Our CNN is able to identify if a vein fully compresses with a F1 score of more than 90% while applying manual pressure with the ultrasound probe. Fully compressible veins robustly rule out DVT and such patients do not need to be referred to further specialist examination. We have evaluated our method on 1150 5–10 s compression image sequences from 115 healthy volunteers, which results in a data set size of approximately 200k labelled images. Our method yields a theoretical inference frame rate of more than 500 fps and we thoroughly evaluate the performance of 15 possible configurations.
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APA:
Tanno, R., Makropoulos, A., Arslan, S., Oktay, O., Mischkewitz, S., Al-Noor, F.,... Heinrich, M.P. (2018). AutoDVT: Joint real-time classification for vein compressibility analysis in deep vein thrombosis ultrasound diagnostics. In Gabor Fichtinger, Christos Davatzikos, Carlos Alberola-López, Alejandro F. Frangi, Julia A. Schnabel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 905-912). Granada, ESP: Springer Verlag.
MLA:
Tanno, Ryutaro, et al. "AutoDVT: Joint real-time classification for vein compressibility analysis in deep vein thrombosis ultrasound diagnostics." Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Gabor Fichtinger, Christos Davatzikos, Carlos Alberola-López, Alejandro F. Frangi, Julia A. Schnabel, Springer Verlag, 2018. 905-912.
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