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)

Primary Adenocarcinoma in a Seminal Vesicular Cyst: A Case Report

Introduction: Primary adenocarcinoma of the seminal vesicle (SVC) is very rare. Presentation to the case: Herein, we reported a case of SVA of SV cyst arising in a 51-year-old deaf mute male patient. Laboratory parameters including serum prostate specific antigen (PSA) level were normal. Contrast-enhanced magnetic resonance imaging demonstrated large reterovesical cystic lesion with mural nodules. The patient was managed by radical prostatectomy and seminovesiculolectomy.  Microscopic examination revealed well-differentiated primary mucinous adenocarcinoma of left seminal vesicle cyst.   Conclusion: To the best of our knowledge, this was the first case of SVA of SV cyst arising in deaf mute patient

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%

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

Acute vascular rejection after kidney transplantation outcome and effect of different therapeutic modalities

Background: Steroid resistant acute vascular rejection (AVR) is a great obstacle in successful renal transplantation (KTx). The aim of this work was to evaluate the outcome of histologically confirmed acute vascular rejection - which occurred in severe aggressive form in 39 patients following kidney transplantation as well as to study the outcome of therapy. These cases were chosen from 1000 renal allograft recipients who underwent kidney transplantation in the period between March, 1976 and April 1997 in Urology-Nephrology Center, Mansoura, Egypt.Methods: Statistical analysis of risk factors leading to AVR was carried out. The outcome of different rescue therapies used for AVR as well as graft survival functions were also analyzed.Results: Survival analysis for grafts with AVR revealed 60%, 53%, 30 %, 0% graft survival at 1, 2, 5, 10 yrs respectively after Tx. A statistically significant difference was found in comparison to patients who only experienced acute cellular rejection (90%, 84%, 71%, 46% graft survival at 1, 2, 5, 10 years post- KTx respectively) or patients who passed without rejection in their post-transplantation follow up (95%, 91.3%, 83.3%, 65.5% graft survival at 1, 2, 5, 10 yrs respectively). No statistically significant difference on the overall graft survival between the different modalities of therapy was noted. Steroid pulses + plasma exchange were given for 14 patients with AVR, whereas ATG, MAB ± plasma exchange were added to steroid resistant cases (25 patients). Logistic regression analysis of these data showed that prior blood transfusion, donor-recipient consanguinity, retransplantation are the most significant variables related to occurrence of AVR after kidney transplantation. At last follow up, 14 patients 35.9%) were living with functioning grafts, 16 patients (41%) were living on dialysis, 5 patients died with functioning grafts (12.8%) and 4 patients (10.25%) died with failed grafts.In conclusion: AVR remains a major obstacle for renal transplantation as it markedly impaired graft survival and responded poorly to therapy. Prior blood transfusion decreased the incidence of AVR whereas retransplantation and unrelated donation account significantly to the occurrence of AVR after renal Tx

Primary Mucinous Carcinoma of Cowper' gland: A Case Report of a Rare Variant

Introduction: Primary carcinomas of the bulbourethral glands (Cowper’s glands) are extremely rare.Presentation of case: Herein, a 57-year-old man was presented by perineal mass for seven years. Physical examination revealed a nontender stony hard perineal mass without signs of inflammation. A urethrogram showed compression of the anterior part of the bulbous urethra. MRI of the mass revealed large perineal multilocular and marginal enhancement. The patient was managed by excision of the mass with safety margin. Histopathological examination of the mass showed remnants of malignant acini floating in pools of mucin which formed about 80% of tumor tissue Immunohistochemical analysis revealed positive reactions of the tumour cells with cytokeratin 20 but negative reactions for PSA, β-catenin and cytokeratin 7.Conclusion: We reported the first case of primary mucinous carcinoma arising in the Cowper’s glands and the 22nd in the literature of Cowper’s gland carcinoma

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

PHYTOCHEMICAL ANALYSIS, ASSESSMENT OF ANTIPROLIFERATIVE AND FREE RADICAL SCAVENGING ACTIVITY OF MORUS ALBA AND MORUS RUBRA FRUITS

Objective: Mulberry is a nontoxic commonly eaten plant, belongs to the Morus and used in folk medicine in the remedy of dysentery, antiphlogistic, diuretic, expectorant, and antidiabetic. The purpose of this study is to evaluate the antiproliferative and radical scavenging activity of the total alcoholic and successive fractions thereof of Morus alba and Morus rubra fruits. In addition, the chemical composition of the bioactive fractions of each species was investigated.Methods: The antiproliferative potential of 8 extracts on 4 human cancer cell lines, hepatocellular carcinoma (HepG2), Caucasian breast adenocarcinoma (MCF7), prostate (PC3), and colon carcinoma (HCT116) in addition to one normal cell line namely human normal immortalized skin fibroblast cells (BJ1) were carried out. Cell viability was determined using MTT assay. The potency was compared with the reference drug doxorubicin. These extracts were also assayed for 1,1-diphenyl-2-hydrazyl free radical scavenging activities. After saponification of the n-hexane fraction, unsaponifiable matter and fatty acid methyl esters were analyzed by gas liquid chromatography (GLC). The chemical composition of the bioactive fractions was investigated using gas chromatography/mass spectrometry (GC/MS) analysis.Results: All the extracts showed significant free radical scavenging activity dose-dependently. The n-hexane and dichloromethane (DCM) fractions of M. rubra exhibited potent cytotoxic activity on almost cancer cell lines. In the same pattern, ethyl acetate (EtOAc) of M. rubra has moderate cytotoxic activity against all cell lines except HepG2. DCM fraction of M. alba possessed both radical scavenging and high potential antiproliferated activities against HCT116 and MCF7 with inhibitory concentration of 43.9 and 32.3 μg/ml, respectively, while it showed no cytotoxic effect on BJ1. GLC analysis showed the major hydrocarbons in M. alba and M. rubra were heptacosane and docosane, respectively. Sterols were similar in both species but with different ratios and cholesterol was the major one. Palmitic and margaric were the major saturated fatty acid while arachidonic was the major unsaturated fatty acid in both species. GC/MS analysis showed the main compound in DCM fraction of each Morus species was palmitic acid. Furthermore, 1,11-bis-(methoxycarbonyl-ethenyl)-10,2-dihydroxy-cycloeicosane and linolelaidic acid, methyl ester were the main compounds in the EtOAc fraction of each Morus species. Whereas, the main compounds in alcoholic extract of M. alba and M. rubra were methyl-14-methyl-pentadecanoate and 1,2-O-isopropylyidene-4-nonene-1,2,3-triol, respectively.Conclusions: The results observed remarkable biological activity of the successive fractions of M. rubra more than those of M. alba and confirmed its importance as a natural bioactive source. Morus species are good candidates to be promising as possible sources for future antitumor and antioxidants in food and pharmaceutical formulations. The strong activity partly explains the potential effects of Morus species for the treatment of cancer and degenerative diseases caused by free radicals

Precise Cerebrovascular Segmentation

© 2020 IEEE. Analyzing cerebrovascular changes using Time-of-Flight Magnetic Resonance Angiography (ToF-MRA) images can detect the presence of serious diseases and track their progress, e.g., hypertension. Such analysis requires accurate segmentation of the vasculature from the surroundings, which motivated us to propose a fully automated cerebral vasculature segmentation approach based on extracting both prior and current appearance features that capture the appearance of macro and micro-vessels. 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, while the current appearance is represented with a marginal probability distribution of voxel intensities by using a Linear Combination of Discrete Gaussians (LCDG) whose parameters are estimated by a modified Expectation-Maximization (EM) algorithm. The proposed approach was validated on 190 data sets using three metrics, which revealed high accuracy compared to existing approaches