AutoDVT: Joint real-time classification for vein compressibility analysis in deep vein thrombosis ultrasound diagnostics

Ryutaro Tanno, Antonios Makropoulos, Salim Arslan, Ozan Oktay, Sven Mischkewitz, Fouad Al-Noor, Jonas Oppenheimer, Ramin Mandegaran, Bernhard Kainz*, Mattias P. Heinrich

*Corresponding author for this work


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.

Original languageEnglish
Title of host publicationMICCAI 2018: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
EditorsAlejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger
Number of pages8
Volume11071 LNCS
PublisherSpringer, Cham
Publication date26.09.2018
ISBN (Print)978-3-030-00933-5
ISBN (Electronic)978-3-030-00934-2
Publication statusPublished - 26.09.2018
Eventthe 21st International Conference on Medical Imaging and Computer-Assisted Intervention - Granada, Spain
Duration: 16.09.201820.09.2018
Conference number: 218619


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