Cerebrovascular segmentation from MRA images

There is provided a method of processing a cerebrovascular medical image, the method comprising receiving magnetic resonance angiography (MRA) image associated with a cerebrovascular tissue comprising blood vessels and brain tissues other than blood vessels; segmenting MRA image using a prior appearance model for generating first prior appearance features representing a first-order prior appearance model and second appearance features representing a second-order prior appearance model of the cerebrovascular tissue, wherein current appearance model comprises a 3D Markov-Gibbs Random Field (MGRF) having a 2D rotational and translational symmetry such that MGRF model is 2D rotation and translation invariant; segmenting MRA image using current appearance model for generating current appearance features distinguishing blood vessels from other brain tissues; adjusting MRA image using first and second prior appearance features and current appearance futures; and generating an enhanced MRA image based on said adjustment. There is also provided a system for doing the same. Application US16/159,790 events 2018-10-15 Application filed by Zayed University 2018-10-15 Priority to US16/159,790 2018-10-15 Assigned to Zayed University 2020-04-16 Publication of US20200116808A1 2020-09-08 Application granted 2020-09-08 Publication of US10768259B2 Status Active 2039-03-02 Adjusted expiratio

A Review on the Cerebrovascular Segmentation Methods

© 2018 IEEE. This paper explores various methods that have been proposed for the segmentation of the cerebrovascular structure. All of the methods listed are a combination old, new, automatic and semiautomatic models that produce promising results. Each method will be explained along with its advantages and disadvantages. Each of the methods explained are further explored in this paper with variety algorithms produced by using certain models to target certain areas in the cerebrovascular structure. These algorithms were developed to segment cerebrovascular structures from scans obtained from various image modalities e.g., time of flight magnetic-resonance angiography (TOF-MRA), and computed tomography angiography (CTA)

Early Diagnosis and Staging of Prostate Cancer Using Magnetic Resonance Imaging: State of the Art and Perspectives

Prostate cancer is the second most common cancer among men in the United States after skin cancer. Although it can be a serious disease, early diagnosis of prostate cancer can significantly prevent the growth of cancerous cells. The feature extraction is the process of defining and deriving from the prostate region computational entities that form a sort of prostate cancer signature. Full computer-aided diagnosis (CAD) systems presented in several studies have reported the use of engineered features obtained from multimodal magnetic resonance imaging (MRI) to detect prostate cancer. Similar to other medical imaging CAD systems, the computer-aided diagnosis of prostate cancer using MRI framework encompasses four stages, namely: pre-processing, prostate region extraction, features extraction, and classification. Identifying the region of interest in the MR images is essential to reduce the complexity of the next stages and enhance the performance of the overall CAD system

Deep Learning Based Method for Computer Aided Diagnosis of Diabetic Retinopathy

© 2019 IEEE. Diabetic retinopathy (DR) is a retinal disease caused by the high blood sugar levels that may damage and block the blood vessels feeding the retina. In the early stages of DR, the disease is asymptomatic; however, as the disease advances, a possible sudden loss of vision and blindness may occur. Therefore, an early diagnosis and staging of the disease is required to possibly slow down the progression of the disease and improve control of the symptoms. In response to the previous challenge, we introduce a computer aided diagnosis tool based on convolutional neural networks (CNN) to classify fundus images into one of the five stages of DR. The proposed CNN consists of a preprocessing stage, five stage convolutional, rectified linear and pooling layers followed by three fully connected layers. Transfer learning was adopted to minimize overfitting by training the model on a larger dataset of 3.2 million images (i.e. ImageNet) prior to the use of the model on the APTOS 2019 Kaggle DR dataset. The proposed approach has achieved a testing accuracy of 77% and a quadratic weighted kappa score of 78%, offering a promising solution for a successful early diagnose and staging of DR in an automated fashion

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%

A Novel Fully Automated CAD System for Left Ventricle Volume Estimation

© 2018 IEEE. Left ventricular (LV) volumes, and emptying and filling function remain important indices in conditions such as heart failure. These parameters are derived from the volume curve contained by the inner border of the LV of the heart, throughout the emptying and filling phases of the cardiac cycle, and the peak emptying and filling rates. The gold standard uses the Simpson rule to estimate volume from stacks of short axis images acquired using cine MRI. In this study, a deep learning, automated supervised approach to estimate ventricular volumes is introduced. Unlike prior methods that required hand-crafted image features to segment the inner contour, the proposed approach uses an automatically selected region of interest (ROI), and intelligently determines the optimum features directly from the ROI information. These derived features are then inputted into a deep learning regression model, with the estimated volume as the output results

Left ventricle segmentation and quantification using deep learning

Cardiac MRI is a widely used noninvasive tool that can provide us with an evaluation of cardiac anatomy and function. It can also be used for heart diagnosis. Heart diagnosis through the estimation of physiological heart parameters requires careful segmentation of the left ventricle (LV) from the images of cardiac MRI. Therefore we aim at building a new deep learning method for the automated delineation and quantification of the LV from cine cardiac MRI. Our goal is to reach lower errors for the calculated heart parameters than the previous works by introducing a new deep learning cardiac segmentation method. Our pipeline starts with an accurate LV localization by finding LV cavity center point using a fully convolutional neural network (FCN) model called FCN1. Then, from all heart sections, we extract a region of interest (ROI) that encompasses the LV. A segmentation for the LV cavity and myocardium is performed from the extracted ROIs using FCN called FCN2. The FCN2 model is associated with multiple bottleneck layers and uses less memory footprint than traditional models such as U-net. Furthermore, we introduced a novel loss function called radial loss that works on minimizing the distance between the ground truth LV contours and the predicted contours. After myocardial segmentation, we estimate the functional and mass parameters of the LV. We used the Automated Cardiac Diagnosis Challenge (ACDC-2017) dataset to validate our pipeline, which provided better segmentation, accurate calculation of heart parameters, and produced fewer errors compared to other approaches applied on the same dataset. Additionally, our segmentation approach showed that it can generalize well across different datasets by validating its performance on a locally collected cardiac dataset. To sum up, we propose a novel deep learning framework that we can translate it into a clinical tool for cardiac diagnosis

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