7 datasets found
  1. a

    MRI Lesion Segmentation in Multiple Sclerosis Database

    • academictorrents.com
    bittorrent
    Updated Apr 8, 2018
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    None (2018). MRI Lesion Segmentation in Multiple Sclerosis Database [Dataset]. https://academictorrents.com/details/e08155e5022d688fea00319bd2ead4f0f703f5bb
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    bittorrent(193085367)Available download formats
    Dataset updated
    Apr 8, 2018
    Authors
    None
    License

    https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified

    Description

    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 );

  2. n

    BrainWeb - Simulated Brain Database

    • neuinfo.org
    • rrid.site
    • +2more
    Updated Jan 29, 2022
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    (2022). BrainWeb - Simulated Brain Database [Dataset]. http://identifiers.org/RRID:SCR_003263
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    Dataset updated
    Jan 29, 2022
    Description

    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.

  3. f

    Results of databases searching.

    • plos.figshare.com
    xlsx
    Updated Dec 5, 2024
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    Omid Mirmosayyeb; Mohammad Yazdan Panah; Yousef Mokary; Mohammad Mohammadi; Elham Moases Ghaffary; Vahid Shaygannejad; Bianca Weinstock-Guttman; Robert Zivadinov; Dejan Jakimovski (2024). Results of databases searching. [Dataset]. http://doi.org/10.1371/journal.pone.0312421.s009
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    xlsxAvailable download formats
    Dataset updated
    Dec 5, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Omid Mirmosayyeb; Mohammad Yazdan Panah; Yousef Mokary; Mohammad Mohammadi; Elham Moases Ghaffary; Vahid Shaygannejad; Bianca Weinstock-Guttman; Robert Zivadinov; Dejan Jakimovski
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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

  4. Brain Tumor MRI Dataset

    • kaggle.com
    Updated Feb 16, 2024
    + more versions
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    Training Data (2024). Brain Tumor MRI Dataset [Dataset]. https://www.kaggle.com/datasets/trainingdatapro/brain-mri-dataset
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 16, 2024
    Dataset provided by
    Kaggle
    Authors
    Training Data
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Description

    Brain Cancer MRI Object Detection & Segmentation Dataset

    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.

    MRI study angles in the dataset

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F5939be1e93e8e0c9f1ff922f184f70fe%2FFrame%2079.png?generation=1707920286083259&alt=media" alt="">

    💴 For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on TrainingData to buy the dataset

    Types of diseases and conditions in the full dataset:

    • Cancer
    • Multiple sclerosis
    • Metastatic lesion
    • Arnold-Chiari malformation
    • Focal gliosis of the brain
    • AND MANY OTHER CONDITIONS

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F0f5a27b8872e85fe23bf742593dc4843%2F2.gif?generation=1707920414940375&alt=media" alt="">

    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.

    OTHER MEDICAL BRAIN MRI DATASETS:

    💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset

    Content

    The dataset includes:

    • ST000001: includes subfolders with 10 studies. Each study includes MRI-scans in .dcm and .jpg formats,
    • DICOMDIR: includes information about the patient's condition and links to access files,
    • Brain_MRI_1.pdf: includes medical report, provided by the radiologist,
    • .csv file: includes id of the studies and the number of files

    Medical reports include the following data:

    • Patient's demographic information,
    • Description of the case,
    • Preliminary diagnosis,
    • Recommendations on the further actions

    All patients consented to the publication of data

    Medical data might be collected in accordance with your requirements.

    TrainingData provides high-quality data annotation tailored to your needs

    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

  5. f

    JBI risk of bias assessment for cross-sectional studies.

    • plos.figshare.com
    xls
    Updated Apr 16, 2024
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    Nima Broomand Lomer; Kamal AmirAshjei Asalemi; Alia Saberi; Kasra Sarlak (2024). JBI risk of bias assessment for cross-sectional studies. [Dataset]. http://doi.org/10.1371/journal.pone.0300415.t003
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    xlsAvailable download formats
    Dataset updated
    Apr 16, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Nima Broomand Lomer; Kamal AmirAshjei Asalemi; Alia Saberi; Kasra Sarlak
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    JBI risk of bias assessment for cross-sectional studies.

  6. f

    Brain regions suggested to be involved in depression and/or fatigue...

    • plos.figshare.com
    xls
    Updated Mar 29, 2024
    + more versions
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    Agniete Kampaite; Rebecka Gustafsson; Elizabeth N. York; Peter Foley; Niall J. J. MacDougall; Mark E. Bastin; Siddharthan Chandran; Adam D. Waldman; Rozanna Meijboom (2024). Brain regions suggested to be involved in depression and/or fatigue symptomatology in pwRRMS structural connectivity measures, in 10/18 publications with positive findings. [Dataset]. http://doi.org/10.1371/journal.pone.0299634.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Mar 29, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Agniete Kampaite; Rebecka Gustafsson; Elizabeth N. York; Peter Foley; Niall J. J. MacDougall; Mark E. Bastin; Siddharthan Chandran; Adam D. Waldman; Rozanna Meijboom
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Brain regions suggested to be involved in depression and/or fatigue symptomatology in pwRRMS structural connectivity measures, in 10/18 publications with positive findings.

  7. Overview of study characteristics and findings for included publications (N...

    • plos.figshare.com
    xls
    Updated Mar 29, 2024
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    Agniete Kampaite; Rebecka Gustafsson; Elizabeth N. York; Peter Foley; Niall J. J. MacDougall; Mark E. Bastin; Siddharthan Chandran; Adam D. Waldman; Rozanna Meijboom (2024). Overview of study characteristics and findings for included publications (N = 60) in the current systematic review. [Dataset]. http://doi.org/10.1371/journal.pone.0299634.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Mar 29, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Agniete Kampaite; Rebecka Gustafsson; Elizabeth N. York; Peter Foley; Niall J. J. MacDougall; Mark E. Bastin; Siddharthan Chandran; Adam D. Waldman; Rozanna Meijboom
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Overview of study characteristics and findings for included publications (N = 60) in the current systematic review.

  8. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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None (2018). MRI Lesion Segmentation in Multiple Sclerosis Database [Dataset]. https://academictorrents.com/details/e08155e5022d688fea00319bd2ead4f0f703f5bb

MRI Lesion Segmentation in Multiple Sclerosis Database

Explore at:
20 scholarly articles cite this dataset (View in Google Scholar)
bittorrent(193085367)Available download formats
Dataset updated
Apr 8, 2018
Authors
None
License

https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified

Description

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 );

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