scholarly journals A Deep Learning-Based Approach for Detection of Dementia from Brain Mri

2021 ◽  
Vol 23 (07) ◽  
pp. 516-529
Author(s):  
Reshma L ◽  
◽  
Sai Priya Nalluri ◽  
Priya R Sankpal ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Support Vector ◽  
Imaging Data ◽  
Diagnosis Of Dementia ◽  
User Friendly ◽  
Clustering Pattern ◽  
Medical Analysis ◽  
The Brain

In this paper, a user-friendly system has been developed which will provide the result of medical analysis of digital images like magnetization resonance of image scan of the brain for detection and classification of dementia. The small structural differences in the brain can slowly and gradually become a major disease like dementia. The progression of dementia can be slowed when identified early. Hence, this paper aims at developing a robust system for classification and identifying dementia at the earliest. The method used in this paper for initial disclosure and diagnosis of dementia is deep learning since it can give important results in a shorter period of time. Deep Learning methods such as K-means clustering, Pattern Recognition, and Multi-class Support Vector Machine (SVM) have been used to classify different stages of dementia. The goal of this study is to provide a user interface for deep learning-based dementia classification using brain magnetic resonance imaging data. The results show that the created method has an accuracy of 96% and may be utilized to detect people who have dementia or are in the early stages of dementia.

scholarly journals 3D U-Net for Skull Stripping in Brain MRI

2019 ◽  
Vol 9 (3) ◽  
pp. 569 ◽  
Author(s):  
Hyunho Hwang ◽  
Hafiz Zia Ur Rehman ◽  
Sungon Lee
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Brain Extraction ◽  
Skull Stripping ◽  
Segmentation Algorithms ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Network ◽  
The Brain

Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953.

A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques

2020 ◽  
Vol 17 (4) ◽  
pp. 1925-1930
Author(s):  
Ambeshwar Kumar ◽  
R. Manikandan ◽  
Robbi Rahim
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Convolution Algorithm ◽  
The Brain

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process

2020 ◽  
Vol 30 (06) ◽  
pp. 2050032
Author(s):  
Wei Feng ◽  
Nicholas Van Halm-Lutterodt ◽  
Hao Tang ◽  
Andrew Mecum ◽  
Mohamed Kamal Mesregah ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Disease Process ◽  
Disease Classification ◽  
Support Vector ◽  
Imaging Data ◽  
Clinical Tool ◽  
3D Cnn

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.

scholarly journals OTHR-13. A DEEP LEARNING APPROACH TO DETECT CANCER METASTASES TO THE BRAIN IN MRI

2019 ◽  
Vol 1 (Supplement_1) ◽  
pp. i20-i21
Author(s):  
Min Zhang ◽  
Geoffrey Young ◽  
Huai Chen ◽  
Lei Qin ◽  
Xinhua Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Metastases ◽  
False Positive ◽  
False Positive Rate ◽  
Brain Mri ◽  
Ground Truth ◽  
Learning Approach ◽  
Cancer Metastases ◽  
The Brain

Abstract BACKGROUND AND OBJECTIVE: Brain metastases have been found to account for one-fourth of all cancer metastases seen in clinics. Magnetic resonance imaging (MRI) is widely used for detecting brain metastases. Accurate detection of the brain metastases is critical to design radiotherapy to treat the cancer and monitor their progression or response to the therapy and prognosis. However, finding metastases on brain MRI is very challenging as many metastases are small and manifest as objects of weak contrast on the images. In this work we present a deep learning approach integrated with a classification scheme to detect cancer metastases to the brain on MRI. MATERIALS AND METHODS: We retrospectively extracted 101 metastases patients, equal to 1535 metastases on 10192 slices of images in a total of 336 scans from our PACS and manually marked the lesions on T1-weighted contrast enhanced MRI as the ground-truth. We then randomly separated the cases into training, validation, and test sets for developing and optimizing the deep learning neural network. We designed a 2-step computer-aided detection (CAD) pipeline by first applying a fast region-based convolutional neural network method (R-CNN) to sequentially process each slice of an axial brain MRI to find abnormal hyper-intensity that may correspond to a brain metastasis and, second, applying a random under sampling boost (RUSBoost) classification method to reduce the false positive metastases. RESULTS: The computational pipeline was tested on real brain images. A sensitivity of 97.28% and false positive rate of 36.25 per scan over the images were achieved by using the proposed method. CONCLUSION: Our results demonstrated the deep learning-based method can detect metastases in very challenging cases and can serve as CAD tool to help radiologists interpret brain MRIs in a time-constrained environment.

scholarly journals Pelizaeus-Merzbacher Disease: A Case Report

2020 ◽  
Author(s):  
Ghazaleh Jamalipour Soufi ◽  
Siavash Iravan
Keyword(s):  
Case Report ◽  
White Matter ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Proteolipid Protein ◽  
Radiology Department ◽  
Magnetic Resonance Imaging Mri ◽  
Normal White Matter ◽  
Pelizaeus Merzbacher Disease ◽  
The Brain

Pelizaeus-Merzbacher Disease (PMD), as a rare genetically x-linked leukodystrophy, is a disorder of proteolipid protein expression in myelin formation. This disorder is clinically presented by neurodevelopmental delay and abnormal pendular eye movements. The responsible gene for this disorder is the proteolipid protein gene (PLP1). Our case was a oneyear-old boy referred to the radiology department for evaluating the Central Nervous System (CNS) development by brain Magnetic Resonance Imaging (MRI). Clinically, he demonstrated neuro-developmental delay symptoms. The brain MRI results indicated a diffuse lack of normal white matter myelination. This case report should be considered about the possibilityof PMD in the brain MRI of patients who present a diffuse arrest of normal white matter myelination.

Brain MRI Intelligent Diagnostic Using an Improved Deep Convolutional Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 836-845
Author(s):  
Xiangsheng Zhang ◽  
Feng Pan ◽  
Leyuan Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Tissue ◽  
Brain Mri ◽  
Deep Convolutional Neural Network ◽  
Brain Diseases ◽  
Support Vector ◽  
Svm Classifier ◽  
Intelligent Diagnosis ◽  
The Brain

The diagnosis of brain diseases based on magnetic resonance imaging (MRI) is a mainstream practice. In the course of practical treatment, medical personnel observe and analyze the changes in the size, position, and shape of various brain tissues in the brain MRI image, thereby judging whether the brain tissue has been diseased, and formulating the corresponding medical plan. The conclusion drawn after observing the image will be influenced by the subjective experience of the experts and is not objective. Therefore, it has become necessary to try to avoid subjective factors interfering with the diagnosis. This paper proposes an intelligent diagnosis model based on improved deep convolutional neural network (IDCNN). This model introduces integrated support vector machine (SVM) into IDCNN. During image segmentation, if IDCNN has problems such as irrational layer settings, too many parameters, etc., it will make its segmentation accuracy low. This study made a slight adjustment to the structure of IDCNN. First, adjust the number of convolution layers and down-sampling layers in the DCNN network structure, adjust the network’s activation function, and optimize the parameters to improve IDCNN’s non-linear expression ability. Then, use the integrated SVM classifier to replace the original Softmax classifier in IDCNN to improve its classification ability. The simulation experiment results tell that compared with the model before improvement and other classic classifiers, IDCNN improves segmentation results and promote the intelligent diagnosis of brain tissue.

The association between aortic regurgitation and undetermined embolic infarction with aortic complex plaque

2017 ◽  
Vol 13 (4) ◽  
pp. 391-399 ◽  
Author(s):  
Dae-Won Kim ◽  
Jung Sun Cho ◽  
Jae Yeong Cho ◽  
Kye Hun Kim ◽  
Byung Joo Sun ◽  
...  
Keyword(s):  
Transesophageal Echocardiography ◽  
Aortic Regurgitation ◽  
Thoracic Aorta ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
High Mobility ◽  
Embolic Stroke ◽  
Bubble Test ◽  
Aortic Plaques ◽  
The Brain

Background Retrograde embolism from the descending thoracic aorta is one possible cause of undetermined ischemic stroke. Significant aortic regurgitation can increase the amount of reversed flow in the thoracic aorta and thus is associated with an increased incidence of stroke. Aims This study aimed to examine the association between significant aortic regurgitation and undetermined embolic infarction with aortic complex plaques. Methods This study included 380 patients with undetermined embolic stroke who did not have abnormal flow such as atrial septal defect, patent foramen ovale determined by agitated saline bubble test, intracardiac thrombi on transesophageal echocardiography, atrial fibrillation, or small vessel stroke, cerebral artery, and carotid stenosis on the brain magnetic resonance imaging. The patients were divided into the complex aortic plaques group (n = 63), which was defined as having plaque with >4 mm in thickness, ulceration, or high mobility, and the no complex aortic plaques group (n = 317). Results Transesophageal echocardiography with a bubble study, brain MRI, and laboratory tests were performed for all subjects. Significant aortic regurgitation was more prevalent in patients with undetermined embolic stroke and complex aortic plaques than in patients without complex aortic plaques (adjusted OR = 4.981; 95% CI = 1.323–18.876, P = 0.028). In addition, the distribution of complex aortic plaques according to the severity of aortic regurgitation in patients with undetermined embolic stroke had a tendency toward the ascending thoracic aorta and proximal aortic arch. Conclusions Significant aortic regurgitation may affect undetermined embolic stroke in patients with complex aortic plaques.

scholarly journals DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data

2020 ◽  
Vol 19 (1) ◽  
pp. 159-184
Author(s):  
Sebastian R. van der Voort ◽  
◽  
Marion Smits ◽  
Stefan Klein
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Data Curation ◽  
Proton Density ◽  
Sorting Algorithm ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Post Contrast

AbstractWith the increasing size of datasets used in medical imaging research, the need for automated data curation is arising. One important data curation task is the structured organization of a dataset for preserving integrity and ensuring reusability. Therefore, we investigated whether this data organization step can be automated. To this end, we designed a convolutional neural network (CNN) that automatically recognizes eight different brain magnetic resonance imaging (MRI) scan types based on visual appearance. Thus, our method is unaffected by inconsistent or missing scan metadata. It can recognize pre-contrast T1-weighted (T1w),post-contrast T1-weighted (T1wC), T2-weighted (T2w), proton density-weighted (PDw) and derived maps (e.g. apparent diffusion coefficient and cerebral blood flow). In a first experiment,we used scans of subjects with brain tumors: 11065 scans of 719 subjects for training, and 2369 scans of 192 subjects for testing. The CNN achieved an overall accuracy of 98.7%. In a second experiment, we trained the CNN on all 13434 scans from the first experiment and tested it on 7227 scans of 1318 Alzheimer’s subjects. Here, the CNN achieved an overall accuracy of 98.5%. In conclusion, our method can accurately predict scan type, and can quickly and automatically sort a brain MRI dataset virtually without the need for manual verification. In this way, our method can assist with properly organizing a dataset, which maximizes the shareability and integrity of the data.

scholarly journals The Missing Link in the Pathophysiology of Vascular Cognitive Impairment: Design of the Heart-Brain Study

2017 ◽  
Vol 7 (3) ◽  
pp. 140-152 ◽  
Author(s):  
Astrid M. Hooghiemstra ◽  
Anne Suzanne Bertens ◽  
Anna E. Leeuwis ◽  
Esther E. Bron ◽  
Michiel L. Bots ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Neuropsychological Testing ◽  
Brain Mri ◽  
Vascular Cognitive Impairment ◽  
Imaging Data ◽  
Hemodynamic Changes ◽  
Hemodynamic Status ◽  
The Brain ◽  
Brain Study

Background: Hemodynamic balance in the heart-brain axis is increasingly recognized as a crucial factor in maintaining functional and structural integrity of the brain and thereby cognitive functioning. Patients with heart failure (HF), carotid occlusive disease (COD), and vascular cognitive impairment (VCI) present themselves with complaints attributed to specific parts of the heart-brain axis, but hemodynamic changes often go beyond the part of the axis for which they primarily seek medical advice. The Heart-Brain Study hypothesizes that the hemodynamic status of the heart and the brain is an important but underestimated cause of VCI. We investigate this by studying to what extent hemodynamic changes contribute to VCI and what the mechanisms involved are. Here, we provide an overview of the design and protocol. Methods: The Heart-Brain Study is a multicenter cohort study with a follow-up measurement after 2 years among 645 participants (175 VCI, 175 COD, 175 HF, and 120 controls). Enrollment criteria are the following: 1 of the 3 diseases diagnosed according to current guidelines, age ≥50 years, no magnetic resonance contraindications, ability to undergo cognitive testing, and independence in daily life. A core clinical dataset is collected including sociodemographic factors, cardiovascular risk factors, detailed neurologic, cardiac, and medical history, medication, and a physical examination. In addition, we perform standardized neuropsychological testing, cardiac, vascular and brain MRI, and blood sampling. In subsets of participants we assess Alz­heimer biomarkers in cerebrospinal fluid, and assess echocardiography and 24-hour blood pressure monitoring. Follow-up measurements after 2 years include neuropsychological testing, brain MRI, and blood samples for all participants. We use centralized state-of-the-art storage platforms for clinical and imaging data. Imaging data are processed centrally with automated standardized pipelines. Results and Conclusions: The Heart-Brain Study investigates relationships between (cardio-)vascular factors, the hemodynamic status of the heart and the brain, and cognitive impairment. By studying the complete heart-brain axis in patient groups that represent components of this axis, we have the opportunity to assess a combination of clinical and subclinical manifestations of disorders of the heart, vascular system and brain, with hemodynamic status as a possible binding factor.

scholarly journals The ANTsX ecosystem for quantitative biological and medical imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicholas J. Tustison ◽  
Philip A. Cook ◽  
Andrew J. Holbrook ◽  
Hans J. Johnson ◽  
John Muschelli ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Open Source Software ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Network Architectures ◽  
Imaging Data ◽  
Software Libraries ◽  
Learning Capabilities

AbstractThe Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.

Sign in / Sign up

Export Citation Format

Share Document