Bimodal automated carotid ultrasound segmentation using geometrically constrained deep neural networks

For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essential part in this monitoring process. In this paper, we propose a novel Deep Neural Network as a fully automated segmentation tool, and its application in delineating both the media-adventitia boundary and the lumen-intima boundary. We develop a new geometrically constrained objective function as part of the Network's Stochastic Gradient Descent optimisation, thus tuning it to the problem at hand. Furthermore, we also apply a bimodal fusion of amplitude and phase congruency data proposed by us in previous work, as an input to the network, as the latter provides an intensity-invariant data source to the network. We finally report the segmentation performance of the network on transverse sections of the carotid. Tests are carried out on an augmented dataset of 81,000 images, and the results are compared to other studies by reporting the DICE coefficient of similarity, modified Hausdorff Distance, sensitivity and specificity. Our proposed modification is shown to yield improved results on the standard network over this larger dataset, with the advantage of it being fully automated. We conclude that Deep Neural Networks provide a reliable trained manner in which carotid ultrasound images may be automatically segmented, using amplitude data and intensity invariant phase congruency maps as a data source

Quantifying atherosclerosis in vasculature using ultrasound imaging

Cerebrovascular disease accounts for approximately 30% of the global burden associated with cardiovascular diseases [1]. According to the World Stroke Organisation, there are approximately 13.7 million new stroke cases annually, and just under six million people will die from stroke each year [2]. The underlying cause of this disease is atherosclerosis – a vascular pathology which is characterised by thickening and hardening of blood vessel walls. When fatty substances such as cholesterol accumulate on the inner linings of an artery, they cause a progressive narrowing of the lumen referred to as a stenosis. Localisation and grading of the severity of a stenosis, is important for practitioners to assess the risk of rupture which leads to stroke. Ultrasound imaging is popular for this purpose. It is low cost, non-invasive, and permits a quick assessment of vessel geometry and stenosis by measuring the intima media thickness. Research is showing that 3D monitoring of plaque progression may provide a better indication of sites which are at risk of rupture. Various metrics have been proposed. From these, the quantification of plaques by measuring vessel wall volume (VWV) using the segmented media-adventitia boundaries (MAB) and lumen-intima boundaries (LIB) has been shown to be sensitive to temporal changes in carotid plaque burden. Thus, methods to segment these boundaries are required to help generate VWV measurements with high accuracy, less user interaction and increased robustness to variability in di↵erent user acquisition protocols.ii This work proposes three novel methods to address these requirements, to ultimately produce a highly accurate, fully automated segmentation algorithm which works on intensity-invariant data. The first method proposed was that of generating a novel, intensity-invariant representation of ultrasound data by creating phase-congruency maps from raw unprocessed radio-frequency ultrasound information. Experiments carried out showed that this representation retained the necessary anatomical structural information to facilitate segmentation, while concurrently being invariant to changes in amplitude from the user. The second method proposed was the novel application of Deep Convolutional Networks (DCN) to carotid ultrasound images to achieve fully automatic delineation of the MAB boundaries, in addition to the use of a novel fusion of amplitude and phase congruency data as an image source. Experiments carried out showed that the DCN produces highly accurate and automated results, and that the fusion of amplitude and phase yield superior results to either one alone. The third method proposed was a new geometrically constrained objective function for the network's Stochastic Gradient Descent optimisation, thus tuning it to the segmentation problem at hand, while also developing the network further to concurrently delineate both the MAB and LIB to produce vessel wall contours. Experiments carried out here also show that the novel geometric constraints improve the segmentation results on both MAB and LIB contours. In conclusion, the presented work provides significant novel contributions to field of Carotid Ultrasound segmentation, and with future work, this could lead to implementations which facilitate plaque progression analysis for the end�user

Vision-based estimation of volume status in ultrasound

This thesis provides a proof-of-concept approach to the analysis of ultrasound imagery using machine learning and computer vision for the purposes of tracking relative changes in apparent circulating blood volume. Data for the models was collected from a simulation which involved having healthy subjects recline at angles between 0 and 90 degrees to induce changes in the size of the internal jugular vein (IJV) resulting from gravity. Ultrasound video clips were then captured of the IJV. The clips were segmented, followed by feature generation, feature selection and training of predictive models to determine the angle of inclination. This research provides insight into the feasibility of using automated analysis techniques to enhance portable ultrasound as a monitoring tool. In a dataset of 34 subjects the angle was predicted within 11 degrees. An accuracy of 89% was achieved for high/low classification

Machine learning approaches for early prediction of hypertension.

Hypertension afflicts one in every three adults and is a leading cause of mortality in 516, 955 patients in USA. The chronic elevation of cerebral perfusion pressure (CPP) changes the cerebrovasculature of the brain and disrupts its vasoregulation mechanisms. Reported correlations between changes in smaller cerebrovascular vessels and hypertension may be used to diagnose hypertension in its early stages, 10-15 years before the appearance of symptoms such as cognitive impairment and memory loss. Specifically, recent studies hypothesized that changes in the cerebrovasculature and CPP precede the systemic elevation of blood pressure. Currently, sphygmomanometers are used to measure repeated brachial artery pressure to diagnose hypertension after its onset. However, this method cannot detect cerebrovascular alterations that lead to adverse events which may occur prior to the onset of hypertension. The early detection and quantification of these cerebral vascular structural changes could help in predicting patients who are at a high risk of developing hypertension as well as other cerebral adverse events. This may enable early medical intervention prior to the onset of hypertension, potentially mitigating vascular-initiated end-organ damage. The goal of this dissertation is to develop a novel efficient noninvasive computer-aided diagnosis (CAD) system for the early prediction of hypertension. The developed CAD system analyzes magnetic resonance angiography (MRA) data of human brains gathered over years to detect and track cerebral vascular alterations correlated with hypertension development. This CAD system can make decisions based on available data to help physicians on predicting potential hypertensive patients before the onset of the disease

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf