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

Medical image analysis for the early prediction of hypertension

Recently, medical image analysis has become a vital evolving technology that is used in the early diagnosis of various diseases. Medical imaging techniques enable physicians to capture noninvasive images of structures inside the human body (such as bones, tissues, or blood vessels) as well as their functions (such as brain activity). In this study, magnetic resonance angiography (MRA) images have been analyzed to help physicians in the early prediction of hypertension. Hypertension is a progressive disease that may take several years before being fully understood. In the United States, hypertension afflicts one in every three adults and is a leading cause of mortality in more than half a million patients every year. Specific alterations in human brains’ cerebrovasculature have been observed to precede the onset of hypertension. This study presents a computer-aided diagnosis system (CAD) that can predict hypertension prior to the systemic onset of the disease. This MRA-based CAD system is able to detect, track, and quantify the hypertension-related cerebrovascular alterations, then it makes a decision based on the analyzed data about whether each subject is at a high risk of developing hypertension or not. Such kind of prediction can help clinicians in taking proactive and preventative steps to stop the progress of the disease and mitigate adverse events

Using 3-D CNNs and Local Blood Flow Information to Segment Cerebral Vasculature

© 2018 IEEE. The variability of the strength (increase or decrease) of the blood flow signals throughout the range of slices of the MRA volume is a big challenge for any segmentation approach because it introduces more inhomogenities to the MRA data and hence less accuracy. In this paper, a novel cerebral blood vessel segmentation framework using Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) is proposed to handle this challenge. The segmentation framework is based on using three dimensional convolutional neural networks (3D-CNN) to segment the cerebral blood vessels taking into account the variability of blood flow signals throughout the MRA scans. It consists of the following two steps: i) bias field correction to handle intensity inhomogeneity which are caused by magnetic settings, ii)instead of constructing one CNN model for the whole TOF-MRA brain, the TOF-MRA volume is divided into two compartments, above Circle of Willis (CoW) and at and below CoW to account for blood flow signals variability across the MRA volume\u27s slices, then feed these two volumes into the three dimensional convolutional neural networks (3D-CNN). The final segmentation result is the combination of the output of each model. The proposed framework is tested on in-vivo data (30 TOF-MRA data sets). Both qualitative and quantitative validation with respect to ground truth (delineated by an MRA expert) are provided. The proposed approach achieved a high segmentation accuracy with 84.37% Dice similarity coefficient, sensitivity of 86.14%, and specificity of 99.00%

Analysis of the Importance of Systolic Blood Pressure Versus Diastolic Blood Pressure in Diagnosing Hypertension: MRA Study.

© 2020 IEEE. Hypertension is one of the severest and most common diseases nowadays. It is considered one of the leading contributors to death worldwide. Specialists tend to diagnose hypertension taking into consideration both systolic and diastolic blood pressure (BP) measurements. However, some clinical hypothesis states that under 50 years of age, diastolic may be slightly more predictive of adverse events, while above that age, systolic may be more predictive. The question is should we give more value to systolic BP or diastolic BP when diagnosing diseases such as hypertension? Three different experiments were conducted in this study using magnetic resonance angiography (MRA) data to investigate this question. In each of these experiments, the following methodology was followed: 1) preprocess MRA data to remove noise, bias, or inhomogeneities, 2) segment the cerebral vasculature for each subject using a CNN-based approach, 3) extract vascular features that represent cerebral alterations that precede and accompany the development of hypertension, and 4) finally build feature vectors and classify data into either normotensives or hypertensives based on the cerebral alterations and the blood pressure measurements. The first experiment was conducted on original data set of 342 subjects. While the second and third experiments enlarged the original data set by generating more synthetic samples to make original data set large enough and balanced. Experimental results showed that systolic blood pressure might be more predictive than diastolic blood pressure in diagnosing hypertension with a classification accuracy of 89.3%

A CAD System for the Early Prediction of Hypertension based on Changes in Cerebral Vasculature

© 2019 IEEE. Hypertension is a leading cause for mortality in the US and a significant contributor to many vascular and non vascular diseases. Previous literature reports suggest that specific cerebral vascular alterations precede the onset of hypertension. In this manuscript, we propose a magnetic resonance angiography (MRA)-based computer-aided-diagnosis (CAD) system for the early detection of hypertension. The steps of the proposed CAD system are: 1) preprocessing of the MRA input data to correct the bias resulting from the magnetic field, remove noise effects, reduce contrast non-uniformities, enhance homogeneity using a generalized Gauss-Markov random field (GGMRF), and normalize data to enhance the segmentation process, 2) delineating the cerebral vasculature using a deep 3-D convolutional neural network (CNN) automatically and accurately, 3) extraction of vascular features (cerebrovascular diameters and tortuosity) that are reported to change with the progression of hypertension and constructing the feature vectors, 4) using the feature vectors for classifying input data using a support vector machine (SVM) classifier. We report a 90% classification accuracy in distinguishing between normal and potential hypertensive subjects. These results demonstrate the efficacy of using the proposed vascular features to predict pre-hypertension or hypertension. Clinicians could track the alterations of these vascular features over time for people at risk of developing hypertension for optimal medical management and mitigate adverse events

Accurate Segmentation of Cerebrovasculature from TOF-MRA Images Using Appearance Descriptors

© 2013 IEEE. Analyzing cerebrovascular changes can significantly lead to not only detecting the presence of serious diseases e.g., hypertension and dementia, but also tracking their progress. Such analysis could be better performed using Time-of-Flight Magnetic Resonance Angiography (ToF-MRA) images, but this requires accurate segmentation of the cerebral vasculature from the surroundings. To achieve this goal, we propose a fully automated cerebral vasculature segmentation approach based on extracting both prior and current appearance features that have the ability to capture the appearance of macro and micro-vessels in ToF-MRA. The appearance prior is modeled with a novel translation and rotation invariant Markov-Gibbs Random Field (MGRF) of voxel intensities with pairwise interaction analytically identified from a set of training data sets. The appearance of the cerebral vasculature is also represented with a marginal probability distribution of voxel intensities by using a Linear Combination of Discrete Gaussians (LCDG) that its parameters are estimated by using a modified Expectation-Maximization (EM) algorithm. The extracted appearance features are separable and can be classified by any classifier, as demonstrated by our segmentation results. To validate the accuracy of our algorithm, we tested the proposed approach on in-vivo data using 270 data sets, which were qualitatively validated by a neuroradiology expert. The results were quantitatively validated using the three commonly used metrics for segmentation evaluation: the Dice coefficient, the modified Hausdorff distance, and the absolute volume difference. The proposed approach showed a higher accuracy compared to two of the existing segmentation approaches

Novel Approaches For Segmenting Cerebral Vasculature

In this chapter, we propose two segmentation approaches that are able to segment cerebral vasculature automatically and accurately. This would potentially help experts in the early analysis and diagnosis of severe diseases, specifically, multiple sclerosis

Studying the Role of Cerebrovascular Changes in Different Compartments in Human Brains in Hypertension Prediction

Hypertension is a major cause of mortality of millions of people worldwide. Cerebral vascular changes are clinically observed to precede the onset of hypertension. The early detection and quantification of these cerebral changes would help greatly in the early prediction of the disease. Hence, preparing appropriate medical plans to avoid the disease and mitigate any adverse events. This study aims to investigate whether studying the cerebral changes in specific regions of human brains (specifically, the anterior, and the posterior compartments) separately, would increase the accuracy of hypertension prediction compared to studying the vascular changes occurring over the entire brain’s vasculature. This was achieved by proposing a computer-aided diagnosis system (CAD) to predict hypertension based on cerebral vascular changes that occur at the anterior compartment, the posterior compartment, and the whole brain separately, and comparing corresponding prediction accuracy. The proposed CAD system works in the following sequence: (1) an MRA dataset of 72 subjects was preprocessed to enhance MRA image quality, increase homogeneity, and remove noise artifacts. (2) each MRA scan was then segmented using an automatic adaptive local segmentation algorithm. (3) the segmented vascular tree was then processed to extract and quantify hypertension descriptive vascular features (blood vessels’ diameters and tortuosity indices) the change of which has been recorded over the time span of the 2-year study. (4) a classification module used these descriptive features along with corresponding differences in blood pressure readings for each subject, to analyze the accuracy of predicting hypertension by examining vascular changes in the anterior, the posterior, and the whole brain separately. Experimental results presented evidence that studying the vascular changes that take place in specific regions of the brain, specifically the anterior compartment reported promising accuracy percentages of up to 90%. However, studying the vascular changes occurring over the entire brain still achieve the best accuracy (of up to 100%) in hypertension prediction compared to studying specific compartments

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations

© 2019 The Authors Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the structure of human brains’ cerebrovasculature start to develop years before the onset of hypertension. In this research, we present a novel computer-aided diagnosis (CAD) system for the early detection of hypertension. The proposed CAD system analyzes magnetic resonance angiography (MRA) data of human brains to detect and track the cerebral vascular alterations and this is achieved using the following steps: i) MRA data are preprocessed to eliminate noise effects, correct the bias field effect, reduce the contrast inhomogeneity using the generalized Gauss-Markov random field (GGMRF) model, and normalize the MRA data, ii) the cerebral vascular tree of each MRA volume is segmented using a 3-D convolutional neural network (3D-CNN), iii) cerebral features in terms of diameters and tortuosity of blood vessels are estimated and used to construct feature vectors, iv) feature vectors are then used to train and test various artificial neural networks to classify data into two classes; normal and hypertensive. A balanced data set of 66 subjects were used to test the CAD system. Experimental results reported a classification accuracy of 90.9% which supports the efficacy of the CAD system components to accurately model and discriminate between normal and hypertensive subjects. Clinicians would benefit from the proposed CAD system to detect and track cerebral vascular alterations over time for people with high potential of developing hypertension and to prepare appropriate treatment plans to mitigate adverse events

Segmentation of Infant Brain Using Nonnegative Matrix Factorization

This study develops an atlas-based automated framework for segmenting infants\u27 brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant\u27s brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI