Index

Thesis Description

Acute ischemic stroke (AIS) is among the leading causes of death (9%) and disability worldwide and will increase over the next 20 years [1, 2]. One of the main causes for an AIS is large vessel occlusion (LVO),accounting for more than one-third of all AIS cases [3]. Thus, making it a key component in the diagnostic process and treatment [4]. These occlusions usually occurs in the internal carotid artery, proximal middle cerebral artery or basilar artery. As a treatment for AIS thrombolysis has been used over the years in a cautious manner, due to the limited application time window and the need to asses the risks to benefits ratio individually. An alternative preferred treatment method is the mechanical thrombectomy, where patients are treated surgically [5, 6]. Thereby, doing a fast classification and possibly a detection of LVOs is crucial for a successful diagnosis and thus for the treatment of AIS [4, 6].

Computed Tomography Angiography (CTA) represents one of the most important techniques in order to detect LVOs leading to the diagnosis and treatment management of AIS, by visualizing the cerebrovascular vessel tree [7]. Detecting such LVOs manually is very time consuming, especially when handling big datasets. Hence an automated classification of patients suffering from LVOs is beneficial for the clinical workflow, by shortening the diagnostic time and improving on accurate classification. For this purpose multiple algorithms were implemented and tested for such application using simple machine learning methods, such as regularized logistic regression, linear support vector machines, and random forest, providing good results as presented by Nishi et al. in [8]. In the last couple of years more sophisticated methods like deep learning, in particular convolutional neural networks (CNNs) as seen in [9, 10] are applied and improve previous algorithms. Still traditional deep learning tends to fail to modulate irregular complex structures, such as cerebrovascular vessel trees. This is mainly due to the fact, that the topology differs between patients [11]. Thus, a plausible alternative should be tested.

Graph deep learning has emerged in recent years as a powerful tool in machine learning, using graphs to model geometrical and topological relationships. It provides an advantage over traditional deep learning regarding irregular structures, such as cerebrovascular vessel trees [11]. Another reason for cosidering GDL for detecting LVOs besides the deformations invariance is the dimension reduction of the inputdata and the ability not to use the Volume as a whole. GDL also identifies and explores a wide range of parallelization strategies using simpler neural networks compared to traditional deep learning, leading to speed and memory efficiency [12, 13].

This thesis aims to create a GDL model to automatically detect LVOs, by analyzing the topological structure using distance features to classify them into LVO left, right or no LVO. This approach will be directly
compared to a state-of-the-art LVO CNN model, like in Thamm et al [9]. In summary, the thesis deals with the following points:

Preparation of distance features for vessel graph
Training of GDL models to analyze the graph topology
Implementation of vessel-tree specific augmentation techniques
Analysis and evaluation of the GDL models

References

[1] Walter Johnson, Oyere Onuma, Mayowa Owolabi, and Sonal Sachdev. Stroke: a global response is needed.
Bulletin of the World Health Organization, 94(9):634–634A, 2016.
[2] Geoffrey A. Donnan, Marc Fisher, Malcolm Macleod, and Stephen M. Davis. Stroke. The Lancet,
371(9624):1612–1623, 2008.
[3] Konark Malhotra, Jeffrey Gornbein, and Jeffrey L. Saver. Ischemic Strokes Due to Large-Vessel Occlusions
Contribute Disproportionately to Stroke-Related Dependence and Death: A Review. Frontiers in neurology,
8:1–5, 2017.
[4] Nikita Lakomkin, Mandip Dhamoon, Kirsten Carroll, Inder Paul Singh, Stanley Tuhrim, Joyce Lee, Johanna T. Fifi, and J. Mocco. Prevalence of large vessel occlusion in patients presenting with acute ischemic
stroke: a 10-year systematic review of the literature. Journal of neurointerventional surgery, 11(3):241–245,
2019.
[5] 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.
[6] Salwa El Tawil and Ketih W Muir. Thrombolysis and thrombectomy for acute ischaemic stroke. Royal
College of Physicians, pages 161–165, 2017.
[7] M. D. Ralf W. Baumgartner and et al. Transcranial color–coded duplex sonography, magnetic resonance
angiography, and computed tomography angiography: Methods, applications, advantages, and limitations.
Journal of Clinical Ultrasound, pages 89–111, 2005.
[8] M. D. Hidehisa Nishi, Naoya Oishi, MD, PhD, Akira Ishii, MD, PhD, and et al. Predicting Clinical
Outcomes of Large Vessel Occlusion Before Mechanical Thrombectomy Using Machine Learning. American
Heart Association Journals, pages 2379–2388, 2019.
[9] Florian Thamm, Oliver Taubmann, Markus J¨urgens, Hendrik Ditt, and Andreas Maier. Detection of Large
Vessel Occlusions using Deep Learning by Deforming Vessel Tree Segmentations. EESS.IV, pages 1–7,
2021.
[10] Lucas W. Remedios, Sneha Lingam, Samuel W. Remedios, Riqiang Gao, Stephen W. Clark, Larry T.
Davis, and Bennett A. Landman. Comparison of convolutional neural networks for detecting large vessel
occlusion on computed tomography angiography. Medical physics, 48(10):6060–6068, 2021.
[11] Stavros Georgousis, Michael P. Kenning, and Xianghua Xie. Graph Deep Learning: State of the Art and
Challenges. IEEE Access, 9:22106–22140, 2021.
[12] Si Zhang, Hanghang Tong, Jiejun Xu, and Ross Maciejewski. Graph convolutional networks: a comprehensive review. Computational Social Networks, 6(1):626, 2019.
[13] Federico Monti, Davide Boscaini, and et al. Geometric Deep Learning on Graphs and Manifolds Using
Mixture Model CNNs. IEEE Xplore, (2017):5115–5124.

Erstellung und Evaluierung eines Messprotokolls für die diffusionsgewichtete MRT im Kontext der Screening-basierten Risikostratifikation

1
In Deutschland erkranken rund 10 % aller Frauen im Laufe ihres Lebens an Brustkrebs
[1]. Verschiedene Risikofaktoren tragen zur Entstehung von Brustkrebs bei, wobei 20-
25 % der Frauen durch ihren familiären Hintergrund betroffen sind. Weitere Faktoren,
die zu einem erhöhten Brustkrebsrisiko führen, sind unter anderem ein dichtes Brustdrüsengewebe, ein höheres Alter bei der Geburt des ersten Kindes sowie ein höheres
Lebensalter, circa 85 % der betroffenen Frauen sind über 50 Jahre alt [2, 3].
Zur Brustkrebsfrüherkennung gibt es verschiedenen Verfahren, unter anderem die
Mammographie, die Sonographie, das Abtasten der Brust sowie die Magnetresonanztomographie (MRT).
Gegenüber der Mammographie hat die MRT den großen Vorteil, dass keine ionisierende Strahlung verwendet wird und im Vergleich zur Mammographie vier bis fünf
Krebstodesfälle mehr pro 100 Frauen entdeckt werden [4, 5].
Beim standardisierten MRT-Verfahren wird ein Kontrastmittel verabreicht, welches die
Darstellung sowohl von normalem Brustgewebe als auch von Brustläsionen verbessert. Die Anreicherung des Kontrastmittels im fibroglandulären Gewebes wird als
Background Parenchymal Enhancement (BPE) bezeichnet und ein erhöhter BPE kann
ein starker Indikator für die Wahrscheinlichkeit von Brustkrebs sein [6, 7].
Ein nicht-invasives Verfahren in der MRT, bei der kein Kontrastmittel verabreicht wird
und gleichzeitig die Auswertung der Brustdichte möglich ist, ist die diffusionsgewichtete Magnetresonanztomographie (engl. diffusion weighted imaging, DWI).
Diffusion beschreibt hierbei die zufällige Bewegung von Wassermolekülen aufgrund
von Wärmeenergie [8]. Die DWI misst diese Diffusionsbewegung im Gewebe [9].
Ein wesentlicher Bestandteil der DWI ist der Diffusionskoeffizient. In der freien Diffusion (ohne Einschränkungen der Bewegung) ist es umso wahrscheinlicher, dass die
Wassermoleküle eine längere Distanz zurücklegen, je länger die Diffusionszeit ist. Die
Diffusionsstrecke ist umso größer, je größer der Diffusionskoeffizient ist [8].
Im menschlichen Gewebe ist der Diffusionsprozess jedoch beispielsweise durch Zellwände eingeschränkt, die die Bewegung der Wassermoleküle begrenzen.
Durch diese Beschränkung ist der Diffusionskoeffizient zeitabhängig und wird ADC
(scheinbarer Diffusionskoeffizient, engl. apparent diffusion coefficient) genannt [8]. Bei
einer Brustläsion können die ADC-Werte helfen, bösartige von gutartigen Läsionen
weiter zu unterscheiden [10].
In dieser Masterarbeit geht es um die Optimierung eines MRT-Bildgebungsstandardprotokolls für die weibliche Brust hinsichtlich der Bildqualität und der Stabilität von quantitativen Parametern. Das optimierte Protokoll soll eine verbesserte multidimensionale Analyse des gesunden fibroglandulären Gewebes der Frau in der Makromorphologie ermöglichen sowie einen Einblick in die Gewebemikrostruktur geben.
Des Weiteren soll das optimierte Protokoll mit dem derzeitigen Protokollstandard verglichen werden. Hierzu werden 10 Probandinnen rekrutiert und mit beiden Protokollen
untersucht. Im Anschluss sollen die Daten miteinander verglichen werden.

2
[1] W. Hiddemann und C. R. Bartram. Die Onkologie: Teil 1: Epidemiologie-Pathogenese-Grundprinzipien der Therapie; Teil 2: Solide Tumoren-Lymphome-Leukämien.
Heidelberg: Springer-Verlag, 2. akt. Auflage, S.134, 2009.
[2] K. Rhiem und R. Schmutzler. Risikofaktoren und Prävention des Mammakarzinoms. In Der Onkologe, Vol. 21, No. 3, S. 202 ff, 2015.
[3] M. Kaufmann et al. Die Gynäkologie. Berlin Heidelberg: Springer-Verlag, 3. Auflage,
S.379, 2012.
[4] N. Hylton. Magnetic Resonance Imaging of The Breast: Opportunities to Improve
Breast Cancer Management. In Journal of Clinical Oncology, Vol. 23, No. 8, S.1678,
2005.
[5] M. Jochelson. Advanced Imaging Techniques for the Detection of Breast Cancer.
In American Society of Clinical Oncology Educational Book, Vol. 32, No. 1, S. 65, 2012.
[6] C. S. Giess et al. Background Parenchymal Eenhancement at Breast MR Imaging:
Normal Patterns, Diagnostic Challenges, and Potential for False-Positive and FalseNegative Interpretation. In RadioGraphics, Vol. 34, No. 1, S. 234, 2014.
[7] V. King et al. Background Parenchymal Enhancement at Breast MR Imaging and
Breast Cancer Risk. In Radiology, Vol. 260, No. 1, S.50, 2011.
[8] F. Laun et al. Einführung in die Grundlagen und Techniken der Diffusionsbildgebung. In Der Radiologe, Vol. 51, No. 3, S. 170 ff., 2011.
[9] M. Freitag et al. Ausgewählte klinisch etablierte und wissenschaftliche Techniken
der diffusionsgewichteten MRT. Im Kontext der onkologischen Bildgebung. In Der Radiologe, Vol. 56, No. 2, S. 137, 2016.
[10] G. Jin et al. The Role of Parallel Diffusion-Weighted Imaging and Apparent Diffusion Coeffcient (ADC) Map Values for Evaluating Breast Lesions: Preliminary results.
In Academic Radiology, Vol. 17, No. 4, S. 457, 2010.

Development and Evaluation of an Transformer-based Deep Learning Model for 12-lead ECG Classification

In the field of natural language processing transformer networks, which dispense with recurrent
architectures by using scaled dot-product attention mechanism [1], became state of the art for
many tasks. Due to its huge success, transformers also have been applied in other fields of research
such as music generation or computer vision [2, 3].
For electrocardiogram (ECG) classification convolutional neural networks (CNNs) or recurrent
neural networks (RNNs) are still widely used. Combining a CNN as a feature extractor with
transformer encoders instead of an RNN lately has shown to be potentially competitive with
existing architectures [4]. As transformer layers rely on attention feature maps that can be visualized
easily they could help to improve the interpretability of decisions made by the deep learning
model, which is in particular important in medical and health care applications.
In image classification a recent work proposes that transformers could even replace convolutions
and outperform deep residual models [3]. Therefore the goal of this work is to develop an algorithm
for 12-lead ECG classification with transformer encoder layers as a crucial part of the feature extractor
and evaluate its performance, in particular concerning different types of cardiac abnormalities.
Furthermore, it is to be investigated, if the model learns to compute human-comprehensible
attention feature maps.
The work consists of the following parts:
• Literature research on existing deep learning models for ECG signal classification and arrhythmia
detection.
• Adapt a transformer architecture for 12-lead ECG classification
• Training and evaluation of the model on PTB-XL [5] and ICBEB challenge 2018 [6] data
set
• Comparison based on the ROC-AUC score with a transformer-based reference implementation
[4] and existing models that were benchmarked on PTB-XL [7]
• Assessment of advantages/disadvantages in the classification of different types of cardiac abnormalities,
at morphological and rhythm level in particular, and visualization of attention
maps.

References
[1] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez,
Lukasz Kaiser, and Illia Polosukhin. Attention is all you need.
[2] Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Ian Simon, Curtis
Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, and Douglas Eck.
Music transformer.
[3] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai,
Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly,
Jakob Uszkoreit, and Neil Houlsby. An image is worth 16×16 words: Transformers for image
recognition at scale.
[4] Annamalai Natarajan, Yale Chang, Sara Mariani, Asif Rahman, Gregory Boverman, Shruti
Vij, and Jonathan Rubin. A wide and deep transformer neural network for 12-lead ecg classification.
In 2020 Computing in Cardiology Conference (CinC), Computing in Cardiology
Conference (CinC). Computing in Cardiology, 2020.
[5] PatrickWagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Dieter Kreiseler, Fatima I. Lunze,Wojciech
Samek, and Tobias Schaeffter. PTB-XL, a large publicly available electrocardiography
dataset. Scientific Data, 7(1):154, 2020.
[6] Feifei Liu, Chengyu Liu, Lina Zhao, Xiangyu Zhang, Xiaoling Wu, Xiaoyan Xu, Yulin Liu,
Caiyun Ma, Shoushui Wei, Zhiqiang He, Jianqing Li, and Eddie Ng Yin Kwee. An Open
Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology
Abnormality Detection. Journal of Medical Imaging and Health Informatics, 8(7):1368–1373,
September 2018.
[7] Nils Strodthoff, Patrick Wagner, Tobias Schaeffter, and Wojciech Samek. Deep learning for
ECG analysis: Benchmarks and insights from PTB-XL. IEEE Journal of Biomedical and
Health Informatics, 25(5):1519–1528, 2021.

Towards integration of prior knowledge using spatial transformers for segmentation

Description

The task of segmentation of organs or lesions in medical images such as magnetic resonance or computed tomography plays an important role for clinicians. Based on the annotations, volumes and measures can be made that characterize the patient’s disease state and track it over time. However, manual, or semi-automatic segmentation takes a substantial amount of time, requires experienced annotators, and in some cases has large inter-reader variance. Instead of manually annotating large amounts of data by hand, machine learning-based methods allow to propose segmentations without further attention required. Deep learning-based neural networks have achieved outstanding performance for cell segmentation, organ segmentation, and cardiac segmentation. However, prior information about the organ of interest is typically harder to incorporate into deep learning models. Previous works utilized active shape models (ASM) or relied on the principal component analysis to enforce constraints extracted from prior information [4,5].

In this thesis, spatial transformer networks (STN) [1] will be utilized in combination with reference shapes to obtain predictions heavily utilizing prior information. The dataset proposed for this task consists of 100 three-dimensional volumes of the heart [2] (https://acdc.creatis.insa-lyon.fr/).

[2]

Steps

This thesis can be divided into the following steps:

Training of a segmentation network (e.g., nn U-Net [3]).
Application of a rigid 2D STN to the prediction of the segmentation network + reference shape.
Extension to deformable 2D STN.
Extension of STN to 3D input and application to the segmentation prediction.

Figure 1: Example case of the ACDC dataset with overlaying segmentation masks (left) and 3D rendering of the segmentation mask (right) [2].

Requirements

Good understanding of machine learning/deep learning concepts
Strong knowledge of Python and ideally Pytorch is required

References

[1] Jaderberg, Max, et al. “Spatial transformer networks.” arXiv preprint arXiv:1506.02025 (2015).

[2] O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, et al. “Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved ?” in IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2514-2525, Nov. 2018

[3] Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2020). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 1-9.

[4] Milletari F., Rothberg A., Jia J., Sofka M. (2017) Integrating Statistical Prior Knowledge into Convolutional Neural Networks. In: Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science, vol 10433. Springer, Cham.

[5] Ahmadi, S.-A., Baust, M., Karamalis, A., Plate, A., Boetzel, K., Klein, T., Navab, N.: Midbrain segmentation in transcranial 3D ultrasound for parkinson diagnosis. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 362–369. Springer, Heidelberg (2011)