Automated Quantification of Atherosclerosis in CTA of Carotid Arteries

How is the human body built and how does it function? What are the causes of disease, and where is disease located? Throughout the history of mankind these questions were answered by the use of invasive methods that included the “opening” of the human body, mainly cadavers. Thanks to these invasive techniques the first precise and complete anatomy works started to appear in the 16th century. The most influential works were published by Leonardo da Vinci and the anatomist and physician Andreas Vesalius. The discovery of X-rays in 1895, and their use for medical applications, introduced a new era, in which non-invasive imaging of the functioning human body became feasible. Nowadays, medical imaging includes many different imaging modalities, such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), nuclear and optical imaging, and has become an indispensable diagnostic tool for a wide range of applications. Initially, the application of medical imaging focused on the visualization of anatomy and on the detection and localization of disease. However, with the development of different modalities it has evolved into a much more versatile tool providing important information on e.g. physiology and organ function, biochemistry and metabolism using nuclear imaging (mainly positron emission tomography (PET) imaging), molecular and processes on the molecular and cellular level using molecular imaging techniques

Towards adversarial robustness with 01 lossmodels, and novel convolutional neural netsystems for ultrasound images

This dissertation investigates adversarial robustness with 01 loss models and a novel convolutional neural net systems for vascular ultrasound images. In the first part, the dissertation presents stochastic coordinate descent for 01 loss and its sensitivity to adversarial attacks. The study here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required. In the second part, this dissertation proposes sign activation network with a novel gradient-free stochastic coordinate descent algorithm and its ensembling model. The study here finds that the ensembling model gives a high minimum distortion (as measured by HopSkipJump) compared to full precision, binary, and convolutional neural networks, and explains this phenomenon by measuring the transferability between networks in an ensemble. In the last part, this dissertation tackles three important segmentation problems for vascular ultrasound images with novel convolutional neural networks. More specifically, these three problems are: (1) vessel segmentation in the internal carotid artery, (2) vessel segmentation in the entire carotid system, and (3) vessel and plaque segmentation in the entire carotid system. The study here represents a first successful step towards the automated segmentation of vessel and plaque in carotid artery ultrasound images and is an important step in creating a system that can independently evaluate carotid ultrasounds