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MRI MS DB Description: In the IMT-Segmentation folder there are 38 folders representing data for each patient 38patients). In each patient folder we have: 1) MRI TIFF Images from first and second examination (0 months, 6-12 months) 2) Lesion segmentations (*.plq files). The delineation/s can be loaded into matlab i.e load(file.plq, -.mat ); Then points can be drawn on the image. load( IM_00031_1.plq , -mat );
Database of human brain images derived from a realistic phantom and generated using a sophisticated MRI simulator. Custom simulations may be generated to match a user's selected parameters. The goal is to aid validation of computer-aided quantitative analysis of medical image data. The SBD contains a set of realistic MRI data volumes produced by an MRI simulator. These data can be used by the neuroimaging community to evaluate the performance of various image analysis methods in a setting where the truth is known. The SBD contains simulated brain MRI data based on two anatomical models: normal and multiple sclerosis (MS). For both of these, full 3-dimensional data volumes have been simulated using three sequences (T1-, T2-, and proton-density- (PD-) weighted) and a variety of slice thicknesses, noise levels, and levels of intensity non-uniformity. These data are available for viewing in three orthogonal views (transversal, sagittal, and coronal), and for downloading.
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BackgroundMultiple sclerosis (MS) is a central nervous system disorder marked by progressive neurological impairments. Magnetic resonance imaging (MRI) parameters are key paraclinical measures that play a crucial role in the diagnosis, prognosis, and monitoring of MS-related disability. This study aims to analyze and summarize the existing literature on the correlation between MRI parameters and disability in people with MS (pwMS).MethodsThe PubMed/MEDLINE, Embase, Scopus, and Web of Science databases were searched from inception to July 19, 2024, and a meta-analysis was carried out using R software version 4.4.0 and the random effects model used to determine the pooled correlation coefficient, with its 95% confidence interval (CI), between MRI measurements and disability scales.ResultsAmong 5741 studies, 383 studies with 39707 pwMS were included. The meta-analysis demonstrated that Expanded Disability Status Scale (EDSS) had significant correlations with cervical cord volume (r = -0.51, 95% CI: -0.62 to -0.38, I2 = 0%, p-heterogeneity = 0.86, p-value
Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
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The dataset consists of .dcm files containing MRI scans of the brain of the person with a cancer. The images are labeled by the doctors and accompanied by report in PDF-format.
The dataset includes 10 studies, made from the different angles which provide a comprehensive understanding of a brain tumor structure.
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The MRI scans provide high-resolution images of the anatomical structures, allowing medical professionals to visualize the tumor, its location, size, and surrounding tissues.
The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated tumor detection and segmentation, analyzing tumor response to different treatments, and studying the relationship between imaging features and clinical outcomes.
All patients consented to the publication of data
keywords: tumors, cloud, testing, glioma, related, pytorch, directories, science, improve, directory, malignant, classify, accuracy, level, classified, cancerous, magnetic, neural, resonance, mri brain scan, brain tumor, brain cancer, oncology, neuroimaging, radiology, brain metastasis, glioblastoma, meningioma, pituitary tumor, medulloblastoma, astrocytoma, oligodendroglioma, ependymoma, neuro-oncology, brain lesion, brain metastasis detection, brain tumor classification, brain tumor segmentation, brain tumor diagnosis, brain tumor prognosis, brain tumor treatment, brain tumor surgery, brain tumor radiation therapy, brain tumor chemotherapy, brain tumor clinical trials, brain tumor research, brain tumor awareness, brain tumor support, brain tumor survivor, neurosurgery, neurologist, neuroradiology, neuro-oncologist, neuroscientist, medical imaging, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, computer vision, deep learning
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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JBI risk of bias assessment for cross-sectional studies.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Brain regions suggested to be involved in depression and/or fatigue symptomatology in pwRRMS structural connectivity measures, in 10/18 publications with positive findings.
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Overview of study characteristics and findings for included publications (N = 60) in the current systematic review.
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MRI MS DB Description: In the IMT-Segmentation folder there are 38 folders representing data for each patient 38patients). In each patient folder we have: 1) MRI TIFF Images from first and second examination (0 months, 6-12 months) 2) Lesion segmentations (*.plq files). The delineation/s can be loaded into matlab i.e load(file.plq, -.mat ); Then points can be drawn on the image. load( IM_00031_1.plq , -mat );