Thrombus Detection in Non-Contrast Head CT using Graph Deep Learning

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]. Classication 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 classication 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 specically, 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 classication and graph neural network
models will be investigated to solve it.
In summary, this thesis will comprise the following work items:
ˆ Literature research of state-of-the-art methods for automated thrombus detection
ˆ Extraction of suitable regions of interest based on previously detected clot candidates
ˆ Design and implementation of a (graph) neural network architecture for joint classication of all clot
candidates in a patient
ˆ Investigation of multiple graph structures and model architectures
Master Thesis Antonia Popp
ˆ Optimization and evaluation of the deep learning model
References
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