Thesis Description
Stroke is a severe cerebrovascular disease and one of the major causes of death and disability worldwide [1].
For patients suering from acute stroke, rapid diagnosis and immediate execution of therapeutic measures are
crucial for a successful recovery. In clinical routine, Non-Contrast Computed Tomography (NCCT) is typically
acquired as a rst-line imaging tool to identify the type of the stroke. In case of an acute ischemic infarct,
appropriate therapy planning requires an accurate detection and localization of the occluding blood clot. An
automated detection system would decrease the probability to miss an obstruction, save time and improve the
overall clinical outcome.
Several methods have been proposed to detect large vessel occlusion (LVO) using enhanced CT data like CT
angiography (CTA) [2, 3, 4]. CTA is mainly used in addition to NCCT and enables accurate evaluation of the
occlusion [5]. Nevertheless, studies have shown that the thrombus which causes the occlusion can be detected in
NCCT images due to its abnormal high density structure [6]. Classication from NCCT data can be achieved
by using Convolutional Neural Networks (CNNs) [7]. However, LVOs account for only 24% to 46% of acute
ischemic strokes [8]. Recent approaches for automated intracranial thrombus detection in NCCT are based on
Random Forest classication or CNNs [9, 10]. The results are promising, but further improvement is required
to ensure utility in clinical routine.
This thesis aims to achieve higher reliability in detecting the thrombus on NCCT data, assuming clot localization
in the entire cerebrovascular system. More specically, the goal is to build and improve upon an
existing detection model which applies a 2D U-Net to the slices of a volumetric dataset, consisting of multiple
channels that had been extracted from the raw CT dataset. The locations of the 15 local maxima with the
highest probability in the resulting prediction map are used as potential candidates for the nal prediction
of the thrombus location. The model to be developed shall classify each candidate (as clot / no clot) while
comprehensively considering all candidates found in the patient as well as corresponding regions on the opposite
hemisphere, as this is considered crucial context for the decision. To this end, a region of interest is extracted
around each candidate position and its opposite position obtained by mirroring at the brain mid plane. Each
such region is considered a node and connected with others to form a graph that describes all regions of interest
in a patient. As such, the problem is formulated as a (partial) node classication and graph neural network
models will be investigated to solve it.
In summary, this thesis will comprise the following work items:
Literature research of state-of-the-art methods for automated thrombus detection
Extraction of suitable regions of interest based on previously detected clot candidates
Design and implementation of a (graph) neural network architecture for joint classication of all clot
candidates in a patient
Investigation of multiple graph structures and model architectures
Master Thesis Antonia Popp
Optimization and evaluation of the deep learning model
References
[1] Walter Johnson, Oyere Onuma, Mayowa Owolabi, and Sonal Sachdev. Stroke: a global response is needed.
Bulletin of the World Health Organization, pages 94:634{634A, 2016.
[2] Sunil A. Sheth, Victor Lopez-Rivera, Arko Barman, James C. Grotta, Albert J. Yoo, Songmi Lee,
Mehmet E. Inam, Sean I. Savitz, and Luca Giancardo. Machine learning-enabled automated determination
of acute ischemic core from computed tomography angiography. Stroke, 50(11):3093{3100, 2019.
[3] Matthew T. Stib, Justin Vasquez, Mary P. Dong, Yun Ho Kim, Sumera S. Subzwari, Harold J. Triedman,
Amy Wang, Hsin-Lei Charlene Wang, Anthony D. Yao, Mahesh Jayaraman, Jerrold L. Boxerman, Carsten
Eickho, Ugur Cetintemel, Grayson L. Baird, and Ryan A. McTaggart. Detecting large vessel occlusion at
multiphase CT angiography by using a deep convolutional neural network. Radiology, page 200334, 2020.
[4] Midas Meijs, Frederick J. A. Meijer, Mathias Prokop, Bram van Ginneken, and Rashindra Manniesing.
Image-level detection of arterial occlusions in 4D-CTA of acute stroke patients using deep learning. Medical
image analysis, 66:101810, 2020.
[5] Michael Knauth, Rudiger von Kummer, Olav Jansen, Stefan Hahnel, Arnd Dor
er, and Klaus Sartor.
Potential of CT angiography in acute ischemic stroke. American journal of neuroradiology, 18(6):1001{
1010, 1997.
[6] G. Gacs, A. J. Fox, H. J. Barnett, and F. Vinuela. CT visualization of intracranial arterial thromboembolism.
Stroke, 14(5):756{762, 1983.
[7] Manon L. Tolhuisen, Elena Ponomareva, Anne M. M. Boers, Ivo G. H. Jansen, Miou S. Koopman, Renan
Sales Barros, Olvert A. Berkhemer, Wim H. van Zwam, Aad van der Lugt, Charles B. L. M. Majoie,
and Henk A. Marquering. A convolutional neural network for anterior intra-arterial thrombus detection
and segmentation on non-contrast computed tomography of patients with acute ischemic stroke. Applied
Sciences, 10(14):4861, 2020.
[8] Robert C. Rennert, Arvin R. Wali, Jerey A. Steinberg, David R. Santiago-Dieppa, Scott E. Olson, J. Scott
Pannell, and Alexander A. Khalessi. Epidemiology, natural history, and clinical presentation of large vessel
ischemic stroke. Neurosurgery, 85(suppl 1):S4{S8, 2019.
[9] Patrick Lober, Bernhard Stimpel, Christopher Syben, Andreas Maier, Hendrik Ditt, Peter Schramm, Boy
Raczkowski, and Andre Kemmling. Automatic thrombus detection in non-enhanced computed tomography
images in patients with acute ischemic stroke. Visual Computing for Biology and Medicine, 2017.
[10] Aneta Lisowska, Erin Beveridge, Keith Muir, and Ian Poole. Thrombus detection in (ct brain scans using
a convolutional neural network. In Margarida Silveira, Ana Fred, Hugo Gamboa, and Mario Vaz, editors,
Bioimaging, BIOSTEC 2017, pages 24{33. SCITEPRESS – Science and Technology Publications Lda,
Setubal, 2017.
Thrombus Detection in Non-Contrast Head CT using Graph Deep Learning
Type: MA thesis
Status: finished
Date: October 1, 2020 - April 1, 2021
Supervisors: Andreas Maier, Katharina Breininger, Florian Thamm