Cerebral Vessel Tree Estimation from Non-Contrast CT using Deep Learning Methods

Type: MA thesis

Status: finished

Date: December 1, 2021 - June 1, 2022

Supervisors: Florian Thamm, Dr. Oliver Taubmann (Siemens Healthineers), Andreas Maier

Thesis Description

Globally seen, the WHO (World Health Organization) classifies stroke as the second leading cause of death and the third leading cause of disability [1, 2]. In the United States, on average, every 40 seconds someone has a stroke as statistics from the AHA (American Heart Association) demonstrate. 87% of these strokes are ischemic, the rest is of hemorrhagic nature [3].

The primary first-line neuroimaging technique that is applied in case of a suspected stroke is a non-contrast CT (NCCT) scan. Based on this scan one can differentiate between an ischemic and a hemorrhagic stroke [4]. In case of an acute ischemic stroke a reperfusion can be accomplished either by intravenous thrombolytic drug treatment or with endovascular mechanical thrombectomy. Whereas for thrombolysis the short treatment window and the risk of symptomatic intracranial hemorrhage are limitations, endovascular treatment by using stent retrievers is only securely feasible for large proximal vessel occlusions. Given that, thrombectomy is the preferred method for eligible patients [5, 4]. The decision for or against recanalization by thrombectomy requires the localization of the thrombus on artery-level, which is done using further angiographic imaging [6].

In practice computed tomography angiography (CTA) and magnetic resonance angiography (MRA) are the most important modalities for cerebral angiography. Even though both are in principle suitable for the task, long acquisition time and high operational cost are major drawbacks when it comes to practical application of MRA technique [7]. However, for CTA the patient is exposed to X-rays and intravenous contrast. This contrast agent bears the risk of allergic reactions, contrast-induced nephropathy and thyrotoxicosis [8]. While an angiographic scan is still necessary for thrombus localization, it would be beneficial to also gather as much additional information as possible from the previously acquired NCCT scan. Providing a estimation of the cerebral vessel tree, which is the goal of this thesis, could, for instance, be useful when developing methods for the automatic detection of hyperdense artery signs that can indicate a clot. Such insights can be used to improve decision-making in examination and treatment and serve as a verification for CTA or MRA results. Due to the lack of contrast agent, the vasculature is typically barely visible in NCCT scans, which renders the diagnosis of cerebral ischemia a challenging task [9].

Since the rise of deep learning in medical image analysis, its applications for image segmentation have been a prominent field of research. Especially the U-Net architecture that is designed for fast training on small datasets has gathered huge attention [10]. Previous work addressing cerebral angiography segmentation from NCCT was presented by Klimont et al. [11]. Their approach to generate cerebral angiographies from NCCT scans suffers from several limitations: 1) The segmentation algorithm used to generate the target samples from CTA scans is seen as subpar. 2) The use of 3D-U-Net architecture was not possible due to limited computational resources. Therefore the U-Net is only trained on slices of the scan, which results in a lack of context for the axial dimension. 3) A CycleGAN, which is a deep learning method for image-to-image translation, achieved unsatisfactory results for generating realistic CTA scans. They assume that using an adequate loss function will produce better results [11].

To tackle their first limitation, an already developed, enhanced segmentation algorithm on CTA is applied to provide a better ground truth. Furthermore partitioning the data into patches to enable training with volumetric data on 3D-U-Net is targeted in the first step of this thesis [12]. Then a corresponding CTA should be added as an auxiliary target, which is optimized by extending the previous architecture with a discriminator. This should lead to a more realistic cerebral vessel tree segmentation by the U-Net [13]. A similar architecture has already successfully been applied to a task where synthetic non-contrast images have been generated from CTA scans [14]. Time permitting, a further, optional goal is to train the model to predict separate masks for specific brain vessels (instance segmentation). The evaluation is done on a dataset of 150 patients. For each patient there is a NCCT scan, a CTA scan and a segmentation mask, that is generated out of the given CTA.

The thesis will comprise the following work items:

  1. Literature research on related work
  2. Design, implementation and parametrization of the segmentation model
    1. Cerebral vessel tree segmentation with 3D-U-Net architecture
    2. Addition of CTA as auxiliary target and extension of U-Net with Discriminator
    3. Possibly: Multi-class prediction to predict individual vessel masks
  3. Quantitative evaluation of the implemented system on real-world data (150 samples)


[1] Global Health Estimates 2019: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Technical report, World Health Organization, Geneva, 2020.

[2] Global Health Estimates 2019: Disease burden by Cause, Age, Sex, by Country and by Region, 2000-2019. Technical report, World Health Organization, Geneva, 2020.

[3] Salim S. Virani, Alvaro Alonso, Emelia J. Benjamin, Marcio S. Bittencourt, Clifton W. Callaway, April P.
Carson, Alanna M. Chamberlain, Alexander R. Chang, Susan Cheng, Francesca N. Delling, Luc Djousse,
Mitchell S.V. Elkind, Jane F. Ferguson, Myriam Fornage, Sadiya S. Khan, Brett M. Kissela, Kristen L.
Knutson, Tak W. Kwan, Daniel T. Lackland, Ten´e T. Lewis, Judith H. Lichtman, Chris T. Longenecker, Matthew Shane Loop, Pamela L. Lutsey, Seth S. Martin, Kunihiro Matsushita, Andrew E. Moran,
Michael E. Mussolino, Amanda Marma Perak, Wayne D. Rosamond, Gregory A. Roth, Uchechukwu K.A.
Sampson, Gary M. Satou, Emily B. Schroeder, Svati H. Shah, Christina M. Shay, Nicole L. Spartano,
Andrew Stokes, David L. Tirschwell, Lisa B. VanWagner, Connie W. Tsao, and null null. Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association. Circulation,
141(9):e139–e596, March 2020.

[4] C. Zerna, Z. Assis, C. D. d’Esterre, B. K. Menon, and M. Goyal. Imaging, Intervention, and Workflow in
Acute Ischemic Stroke: The Calgary Approach. American Journal of Neuroradiology, 37(6):978–984, June

[5] Salwa El Tawil and Keith W Muir. Thrombolysis and thrombectomy for acute ischaemic stroke. Clinical
Medicine, 17(2):161–165, April 2017.

[6] Murugan Palaniswami and Bernard Yan. Mechanical Thrombectomy Is Now the Gold Standard for Acute Ischemic Stroke: Implications for Routine Clinical Practice. Interventional Neurology, 4(1-2):18–29, 2015.

[7] D. A. Katz, M. P. Marks, S. A. Napel, P. M. Bracci, and S. L. Roberts. Circle of Willis: evaluation with
spiral CT angiography, MR angiography, and conventional angiography. Radiology, 195(2):445–449, May

[8] James V. Rawson and Allen L. Pelletier. When to Order a Contrast-Enhanced CT. American Family
Physician, 88(5):312–316, September 2013.

[9] Tom van Seeters, Geert Jan Biessels, Joris M. Niesten, Irene C. van der Schaaf, Jan Willem Dankbaar,
Alexander D. Horsch, Willem P. T. M. Mali, L. Jaap Kappelle, Yolanda van der Graaf, Birgitta K. Velthuis,
and on behalf of the Dust Investigators. Reliability of Visual Assessment of Non-Contrast CT, CT Angiography Source Images and CT Perfusion in Patients with Suspected Ischemic Stroke. PLOS ONE,
8(10):e75615, August 2013.

[10] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional Networks for Biomedical
Image Segmentation. In Nassir Navab, Joachim Hornegger, William M. Wells, and Alejandro F. Frangi,
editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Lecture Notes in
Computer Science, pages 234–241, Cham, 2015. Springer International Publishing.

[11] Micha l Klimont, Agnieszka Oronowicz-Ja´skowiak, Mateusz Flieger, Jacek Rzeszutek, Robert Juszkat, and Katarzyna Jo´nczyk-Potoczna. Deep learning for cerebral angiography segmentation from non-contrast computed tomography. PLOS ONE, page 15, July 2020.

[12] Ozg¨un C¸ i¸cek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, and Olaf Ronneberger. 3D U-Net: ¨Learning Dense Volumetric Segmentation from Sparse Annotation. In Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, and William Wells, editors, Medical Image Computing and ComputerAssisted Intervention – MICCAI 2016, Lecture Notes in Computer Science, pages 424–432, Cham, 2016. Springer International Publishing.

[13] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron
Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139–
144, October 2020.

[14] Florian Thamm, Oliver Taubmann, Felix Denzinger, Markus J¨urgens, Hendrik Ditt, and Andreas Maier.
SyNCCT: Synthetic Non-Contrast Images of the Brain from Single-Energy Computed Tomography Angiography. volume 12907 of Lecture Notes in Computer Science. Springer, Cham, September 2021.