Index

Synergistic Radiomics and CNN Features for Multiparametric MRI Lesion Classification

Breast cancer is the most frequent cancer among women, impacting 2.1 million women each year. In order to assist in diagnosing patients with breast cancer, to measure the size of the existing breast tumors and to check for tumors in the opposite breast, breast magnetic resonance imaging (MRI) can be applied. MRI enjoys the advantages that patients won’t suffer from ionizing radiation during the examination, and it can capture the entire breast volume. In the meanwhile, machine learning methods have been proved to accurately classify images by assigning the probability score to estimate the likelihood of an image belonging to a certain category in many fields. With the properties mentioned above, this project aims to investigate whether applying machine learning approaches to breast tumor MRI can provide an accurate prediction on the tumor type (malignant or benign) for the diagnosing purpose.

Dilated deeply supervised networks for hippocampus segmentation in MR

Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer’s Disease (AD). The shape and structure of the hippocampus are important factors in terms of early AD diagnosis and prognosis by clinicians. However, manual segmentation of such subcortical structures in MR studies is a challenging and subjective task. In this paper, we investigate variants of the well known 3D U-Net, a type of convolution neural network (CNN) for semantic segmentation tasks. We propose an alternative form of the 3D U-Net, which uses dilated convolutions and deep supervision to incorporate multi-scale information into the model. The proposed method is evaluated on the task of hippocampus head and body segmentation in an MRI dataset, provided as part of the MICCAI 2018 segmentation decathlon challenge. The experimental results show that our approach outperforms other conventional methods in terms of different segmentation accuracy metrics.

Multimodal Breast Cancer Detection using a Fusion of Ultrasound and Mammogram Features

Classification of Breast Density in Mammograms Using Deep Machine Learning

The female breast is mainly composed of adipose and fibroglandular tissue. In a mammogram, fibroglandular tissue appears brighter than fatty tissue and is therefore called “dense”. Current clinical protocol requires radiologists to not only detect possible cancer tumors but also to evaluate breast density in a mammogram \cite{Wockel.2018}, which corresponds to the relative amount of fibroglandular tissue. Breast density is an important characteristic of a mammogram because it is a breast cancer risk marker and it affects the mammogram’s sensitivity. The evaluation is done via classification into one of the four categories defined by the “Breast Imaging – Reporting and Data System” guidelines from the American College of Radiology (ACR BI-RADS).

In this thesis, the application of convolutional neural networks for the classification of breast density in mammograms is investigated. Several neural network architectures and training methods are tested and the results compared against classical machine learning methods. A strategy for the removal of possibly noisy labels in the training data is presented and an analysis of inter-observer variability among radiologists is carried out. It is found that the algorithm with the best classification performance provides breast density assessment on level with an average experienced radiologist.

COPD Classification in CT Images Using a 3D Convolutional Neural Network

Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. Early detection and diagnosis of COPD can increase the survival rate and reduce the risk of COPD progression in patients. Currently, the primary examination tool to diagnose COPD is spirometry. However, computed tomography (CT) is used for detecting symptoms and sub-type classification of COPD. Using different imaging modalities is a difficult and tedious task even for physicians and is subjective to inter-and intra-observer variations. Hence, developing methods that can automatically classify COPD versus healthy patients is of great interest. In this thesis we propose a 3D deep learning approach to classify COPD and emphysema using volume-wise annotations only. We also investigate the impact of transfer learning on the classification of emphysema using knowledge transfer from a pre-trained COPD classification model.

Tumor Detection & Classification in Breast Cancer Histology Images using Deep Neural Networks

Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation

Mammography uses low-energy X-rays to screen the human breast and is used by radiologists to detect breast cancer. Due to its complexity, a radiologist needs an impeccable image quality. For this reason, the possibility of using deep learning to denoise Mammograms to help radiologists detect breast cancer more easily will be examined. In this thesis, we aim to investigate and develop different deep learning methods for mammogram denoising.
A physically motivated noise model will be simulated on the ground truth images to generate training data. Thereafter the variance stabilizing Anscombe transformation is applied to create white Gaussian noise. Using these data, different network architectures are trained and examined.  For training, a novel loss function will be designed which helps to preserve fine image details crucial for breast cancer detections.
The effectiveness of this loss function is investigated, and its performance is compared again to other state-of-the-art loss functions. It can be shown that the proposed method outperforms state of the art algorithm like BM3D for mammography denoising.  Finally, it will be shown that the network is able to remove not only simulated, but also real noise.

Solution to extend the Field of View of Computed Tomography using Deep Learning approaches

Deep learning has been successfully applied in various applications of computed tomography (CT). Due to limited
detector size and low dose requirements, the problem of data truncation is essentially present in CT. The reconstructed images from such limited field-of-view (FoV) projections suffer from cupping artifacts inside the FoV and distortion or missing of anatomical structures that are outside the FoV [1]. One practical approach to solve the data truncation problem is to apply an extrapolation technique that increases the FoV, then apply an artifact removal technique. The water cylinder extrapolation based reconstruction [2] is a promising method that estimates the projections outside the scan field-of-view (SFoV) by using the knowledge from the projections inside the SFoV. Alternatively, the linear extrapolation technique is the simplest extrapolation technique that always increases the FoV without using any prior information, however, artifacts are still visible on the reconstructed image. Recently, Fourni´e et al. [3] have proposed a deep learning based method “Deep EFoV” to extend the FoV of CT images. First, the FoV is increased by linearly extrapolating the outer channels in the sinogram space. The reconstructed image from this extended FoV sinogram produces artifacts. Finally, the U-net model is used to remove the artifacts in the reconstructed image. The reconstructed image from a neural network model might affect the anatomical structures that are inside the SFoV. To compensate this effect, a standard algorithm “HDFoV” is used where projections inside the SFoV and projections from the neural network model that are outside the FoV are merged.

The aim of the master’s thesis will be to integrate “Deep EFoV” and “HDFoV” algorithms in the C#-based proprietary
reconstruction tool “ReconCT” developed by Siemens Healthineers. The result from the integrated algorithms needs
to be compared with the result from only the “Deep EFoV” algorithm. Another goal is to evaluate and improve the
proposed deep learning model in “Deep EFoV” for the CT FoV extension. The model needs to be improved w. r. t.
tweaking architecture, adapting parameters or even using a different architecture. The dataset and software provided
by Siemens Healthineers will be used in the thesis. The final software needs to be integrated into the “ReconCT” and
has to be presented to the supervisors.

The thesis will include the following points:

• Review of the state-of-the-art method and deep learning approaches to extend the FoV
• Comparison of the proposed method “Deep EFoV” with the integrated “Deep EFoV” and “HDFoV” method
• Improvement and simplification of the proposed deep learning model in “Deep EFoV”
• Integration of the proposed model in the reconstruction tool.

References
[1] Y. Huang, L. Gao, A. Preuhs, and A. Maier, “Field of View Extension in Computed Tomography Using Deep
Learning Prior,” in Bildverarbeitung f¨ur die Medizin: Algorithmen – Systeme – Anwendungen, pp. 186–191,
Springer, 2020.
[2] J. Hsieh, E. Chao, J. Thibault, B. Grekowicz, A. Horst, S. McOlash, and T. J. Myers, “A novel reconstruction
algorithm to extend the CT scan field-of-view,” Medical Physics, vol. 31, no. 9, pp. 2385–2391, 2004.
[3] ´ E. Fourni´e, M. Baer-Beck, and K. Stierstorfer, “CT field of view extension using combined channels extension
and deep learning methods,” in International Conference on Medical Imaging with Deep Learning – Extended
Abstract Track, (London, United Kingdom), 08–10 Jul 2019.

Geometric Deep Learning for Multifocal Diseases

Diseases are classied as multifocal if they are relating to or arising from many foci. They are present in various
medical disciplines, e.g. multifocal atrial tachycardia [1], breast cancer [2] or multifocal motor neuropathy [3].
However, analyzing diseases with multiple centers brings several challenges for conventional deep learning ar-
chitectures. On a technical side, it is complex to handle a varying number of centers which have no unique
sequence. From a medical view, it is important to model structures and relationships between the foci. The grid
structure used in convolutional neural networks cannot handle non-regular neighborhoods. A suitable approach
for this task would be to convert the data into graph structures, where the nodes describe the properties of the
foci and the edges model their mutual relationships. With geometric deep learning, it is possible to learn from
graph structures. It is an emerging eld of research with many possible applications, e.g. classifying documents
in citation graphs or analyzing molecular structures [4]. There also exist several medical applications, e.g. for
analysis of parcinson’s disease [5] or artery segmentation [6]. This thesis aims to investigate the applicability of
this method for relatively small graphs coming from multifocal diseases. The networks are trained to predict
time to events of failure as a metric for the severeness of the disease. Dierent geometric layer architectures,
such as Graph-Attention-Networks [7] and Dierential Pooling [8], are investigated and compared to the per-
formance of a conventional neural network. As we aim to create explicable models, it is intended to provide
visualizations of salient sub-graphs and features of the results. In addition to that, methods to incorporate prior
knowledge from the medical domain into the training process are tested to improve the speed of convergence
and strengthen the medical validity of the predictions. In the end, the networks are tested on liver data.

Summary:

1. Transfer multifocal diseases to meaningful graph structures
2. Provide conventional neural network for time to event regression as baseline
3. Investigate and tune dierent geometric deep learning architectures
4. Visualize salient graph structures

References
[1] Jane F. Desforges and John A. Kastor. Multifocal Atrial Tachycardia. New England Journal of Medicine,
322(24):1713{1717, jun 1990.
[2] John Boyages and Nathan J Coombs. Multifocal and Multicentric Breast Cancer: Does Each Focus Matter?
Article in Journal of Clinical Oncology, 23:7497{7502, 2005.
[3] Eduardo Nobile-Orazio. Multifocal motor neuropathy. Journal of Neuroimmunology, 115(1-2):4{18, apr
2001.
[4] Michael Bronstein, Joan Bruna, Yann Lecun, Arthur Szlam, and Pierre Vandergheynst. Geometric Deep
Learning: Going beyond Euclidean data. IEEE Signal Processing Magazine, 34(4):18{42, 2017.
[5] Xi Zhang, Lifang He, Kun Chen, Yuan Luo, Jiayu Zhou, and Fei Wang. Multi-View Graph Convolutional
Network and Its Applications on Neuroimage Analysis for Parkinson’s Disease. AMIA … Annual Symposium
proceedings. AMIA Symposium, 2018:1147{1156, 2018.
[6] Jelmer M. Wolterink, Tim Leiner, and Ivana Isgum. Graph Convolutional Networks for Coronary Artery
Segmentation in Cardiac CT Angiography. In Lecture Notes in Computer Science (including subseries
Lecture Notes in Articial Intelligence and Lecture Notes in Bioinformatics), volume 11849 LNCS, pages
62{69. Springer, oct 2019.
[7] Petar Velickovic, Arantxa Casanova, Pietro Lio, Guillem Cucurull, Adriana Romero, and Yoshua Bengio.
Graph attention networks. 6th International Conference on Learning Representations, ICLR 2018 – Con-
ference Track Proceedings, pages 1{12, 2018.
[8] Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec. Hierarchi-
cal Graph Representation Learning with Dierentiable Pooling. Advances in Neural Information Processing
Systems, 2018-Decem:4800{4810, jun 2018.

Semi-Supervised Tooth Segmentation in Dental Panoramic Radiographs Using Deep Learning