Thesis Description
Heart diseases, particularly ischemic strokes, are a leading global cause of mortality and morbidity. Atherosclerotic
plaque formation thickens blood vessels walls, serving as a risk indicator for future ischemic stroke
occurrences [1] [2]. Automatic estimation of the vessel wall thickness would offer new potential for screening
patients with respect to high-risk artherosclerotic plaques. The vessel wall thickness can be obtained by segmenting
the vessel wall in cross-section images along the vessels’ centerlines from medical imaging modalities
such as Magnetic Resonance Imaging (MRI) and Computer Tomography (CT). Classical vessel segmentation
methods such as Region Growing [3] and Adaptive Frangi Filtering [4] pose significant challenges as they need to
be manually tuned and do not scale with an increase in data. These issues are adressed by deep learning based
approaches as they do not need manual configuration during inference and can benefit from large amounts of
data. However, popular deep learning based segmentation algorithms such as the U-net pose challenges when
predicting contour points of vessels as these algorithms predict discrete segmentation maps which require further
conversion into the continous domain [5]. Thus, they are dependant on the input resolution and not suitabe to
predict contours on a submillimeter scale. A more suitable way to predict vessel contour points is to perform
deep learning based radius regression on polar unfolded cross-section images as proposed by Ablas et al. [6] or
Chen et al. [7] on black-blood MRI images. The aim of this thesis is to translate the approach to Computer
Tomography data from Photon-Counting CT scanners (PCCT). Furthermore, the thesis aims to exploit the
potential of PCCT scanners and investigate the impact on the prediction of Photon-Counting-based spectral
image information to be able to implicitly suppress non-vessel-like structures. For this purpose, different architectures
(e.g. attention mechanisms [8]) to perform the radius regression are tested and compared against a state
of the art segmentation baseline algorithm on standard inputs. The models are trained and tested on manually
annotated real world CT data. To capture the details of both inner and outer vessel contours, conventional loss
functions and metrics for segmentation, like Intersection-over-Union (IoU), fall short in accommodating subtle
variations in border regions, such as small plaques in the vessel wall. To evaluate the models with respect to
the final application, a suitable distance-based metric should be found that not only accounts for these small
variations but also addresses the multiscale characteristics of the different vessels in the dataset. To ensure
and improve data quality, part of the work will be the iterative training of the model to find errorneous annotations
for manual corrections. Finally, the individual cross-section predictions are converted back to 3D meshes.
Summary:
1. State of the art research
2. Development of a deep learning based algorithm for inner and outer vessel wall contouring on Photon-
Counting CT data
(a) Investigation of the impact of Photon-Counting-based spectral image information
(b) Investigation and optimization of different architectures for vessel contour regression
(c) Investigation of different loss functions and metrics that address the multiscale characteristics and
infrequent occurences of anomalities in the training set
3. Comparison against various baseline algorithms
4. Reconstruction of 3D mesh from contour predictions on 2D cross-sections
5. Evaluation of the developed pipeline and its impact with respect to the final application
References
[1] L. E. Chambless, A. R. Folsom, L. X. Clegg, et al. Carotid wall thickness is predictive of incident clinical
stroke: The atherosclerosis risk in communities (aric) study. American Journal of Epidemiology, 151:478–
487, 3 2000.
[2] Gregory L. Burke, Gregory W. Evans, Ward A. Riley, et al. Arterial wall thickness is associated with
prevalent cardiovascular disease in middle-aged adults. Stroke, 26:386–391, 3 1995.
[3] S.A. Hojjatoleslami and J. Kittler. Region growing: a new approach. IEEE Transactions on Image Processing,
7:1079–1084, 7 1998.
[4] Alejandro Frangi, W J Niessen, Koen Vincken, and Max Viergever. Multiscale vessel enhancement filtering.
Med. Image Comput. Comput. Assist. Interv., 1496, 10 2000.
[5] Florian Thamm, Felix Denzinger, Leonhard Rist, Celia Martin Vicario, Florian Kordon, and Andreas Maier.
Segmentation of the carotid lumen and vessel wall using deep learning and location priors. 1 2022.
[6] Dieuwertje Alblas, Christoph Brune, and Jelmer M. Wolterink. Deep learning-based carotid artery vessel
wall segmentation in black-blood mri using anatomical priors. 12 2021.
[7] Li Chen, Jie Sun, Gador Canton, et al. Automated artery localization and vessel wall segmentation using
tracklet refinement and polar conversion. IEEE Access, 8:217603–217614, 2020.
[8] Wentao Liu, Huihua Yang, Tong Tian, Xipeng Pan, andWeijin Xu. Multiscale attention aggregation network
for 2d vessel segmentation. pages 1436–1440. IEEE, 5 2022.