Computed Tomography Angiography (CTA) continues to be the most widely used imaging modality to visualize
the intracranial vasculature for cerebrovascular disease diagnosis. It is commonly applied in stroke assessment
to determine the underlying cause of the condition.  For the success of treatment approaches like a catheter
intervention, radiologists need to quickly and precisely identify the exact location of the affected artery segments.
Streamlining the process with the help of machine vision could save precious time for the patient. A
crucial step for partly automating the stroke assessment procedure is the accurate labeling of the vessels. Of
particular interest is the Circle of Willis, that combines the main cerebral arteries in a ring-like arrangement
and provides the blood supply for all major brain regions.
There have been multiple attempts to create a reliable classifier for cerebral vasculature, with similar techniques
employed for coronary arteries. The objective is to precisely match artery segments with their anatomical
designation. In most of these methods, the input consists of a 3D CTA or MRA scan of the skull or respectively
the heart. The first type of models are convolutional neural networks (CNN), e.g. U-Net, which directly work
on the images.  These models often have a large number of parameters, which can make training difficult
and slow. In an effort to reduce the amount of data and separate out valuable information, other methods
extract centerline points and the radii of the arteries from the CTA images. The resulting point cloud can
then be processed using a Pointnet++.  However neither of these models incorporates prior knowledge of the
topological structure of the vessels. Another approach involves the construction of a graph from the centerline
points and applying a graph convolutional network (GCN).  Here, the bifurcations of the vessels serve as the
nodes of the graph, while the remaining points yield features of the adjacent edges that represent the segments
between two bifurcations. This model utilizes the connectivity of the arteries, but faces challenges when dealing
with incomplete or missing segments and connections, which are especially common in patients who have
suffered a stroke. In an effort to incorporate local topology information and global context of the vessel graph,
the Topology-Aware Graph Network (TaG-Net) combines a PointNet++ and a GCN.  It uses a PointNet++
layer to encode features for each centerline point, which are subsequently fed into a graph convolutional layer. In
the original paper every point along the centerlines serves as a vertex in the input graph. However, this results
in a high number of nodes and edges, presenting a challenge for effective message passing within the GCN layer.
It remains unclear whether reducing the complexity of the graph could potentially bring an improvement to
The overall goal of this thesis is to find a robust classifier for the accurate labeling of the main cerebral arteries.
In the first step, labels of the vessel graphs from approximately 170 CTA-Scans, that have been annotated
by a heuristic algorithm , need to be corrected manually. Secondly, a Pointnet++ as well as a GCN and a
TaG-Net model will be implemented as baseline methods. Furthermore modifications to the graph structure
of the sample data will be made to possibly improve the utilization of the GCN message passing capabilities.
For the graph convolutional network, this may involve employing an autoencoder to generate informative edge
features. In the case of the TaG-Net, reducing the number of vertices can be achieved by selecting only the
bifurcations as nodes and encoding the remaining points as edge features. Additionally data augmentation
techniques such as introducing missing or incomplete vessel segments, as well as adding corruption and noise
to the data, could improve the robustness of the classifier. All models will be fine-tuned and their performance
1. Improvement of existing vessel segment annotation
2. Implementation and testing of baseline models from literature (PointNet++, GCN, TaG-Net)
3. Improving TaG-Net by exploiting the graph properties of the vessel trees
(a) Restructuring the vessel graph to reduce complexity
(b) Graph-specific data augmentation
4. Fine-tuning and evaluating the models
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