meningiomas

RIDER Neuro MRI contains imaging data on 19 patients with recurrent glioblastoma who underwent repeat imaging sets. These images were obtained approximately 2 days apart (with the exception of one patient, RIDER Neuro MRI-1086100996, whose images were obtained one day apart).

DCE‐MRI: All 19 patients had repeat dynamic contrast‐enhanced MRI (DCE‐MRI) datasets on the same 1.5T imaging magnet. On the basis of T2‐weighted images, technologists chose 16 image locations using 5mm thick contiguous slices for the imaging. For T1 mapping, multi‐flip 3D FLASH images were obtained using flip angles of 5, 10, 15, 20, 25 and 30 degrees, TR of 4.43 ms, TE of 2.1 ms, 2 signal averages. Dynamic images were obtained during the intravenous injection of 0.1mmol/kg of Magnevist intravenous at 3ccs/second, started 24 seconds after the scan had begun. The dynamic images were acquired using a 3D FLASH technique, using a flip angle of 25 degrees, TR of 3.8 ms, TE of 1.8 ms using a 1 x1 x 5mm voxel size. The 16 slice imaging set was obtained every 4.8 sec.

DTI: Seventeen of the 19 patients also obtained repeat diffusion tensor imaging (DTI) sets. Whole brain DTI were obtained using TR 6000ms, TE 100 ms, 90 degree flip angle, 4 signal averages, matrix 128 x 128, 1.72 x 1.72 x 5 mm voxel size, 12 tensor directions, iPAT 2, b value of 1000 sec/mm2 .

Contrast‐enhanced 3D FLASH: All 19 patients underwent whole brain 3D FLASH imaging in the sagittal plane after the administration of Magnevist. For this sequence, TR was 8.6 ms, TE 4.1 ms, 20 degree flip angle, 1 signal average, matrix 256 x 256; 1mm isotropic voxel size.

Contrast‐enhanced 3D FLAIR: All 17 patients who had repeat DTI sets also had 3D FLAIR sequences in the sagittal plane after the administration of Magnevist. For this sequence, the TR was 6000 ms, TE 353 ms, and TI 2200ms; 180 degree flip angle, 1 signal average, matrix 256 x 216; 1 mm isotropic voxel size. Note: before transmission to NCIA, all image sets with 1mm isotropic voxel size were “defaced” using MIPAV software or manually.

About the RIDER project

The Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat "coffee break," longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT] positron emission tomography [PET] CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):

• RIDER White Paper: Combined contracts report ( Sept 2008) PDF

The long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the first RIDER white paper report (2006):

• RIDER White Paper: Executive Summary PDF
• RIDER White Paper: Editorial in Nature.com

This dataset is collected from Kaggle ( https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset ). This dataset is a combination of the following three datasets :figshareSARTAJ datasetBr35H

This brain tumor dataset contains 3064 T1-weighted contrast-inhanced images with three kinds of brain tumor. Detailed information of the dataset can be found in the readme file.The README file is updated:Add image acquisition protocolAdd MATLAB code to convert .mat file to jpg images

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:

• Brain MRI - Multiple Sclerosis Dataset
• Brain Anomaly Detection Dataset
• Normal Brain MRI Dataset
• Fazekas Detection - MRI Dataset

💴 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

This dataset was curated in collaboration between the Computer Science and Engineering Department, University of Dhaka and the National Institute of Neuroscience, Bangladesh. It comprise 5,285 T1-weighted contrast- enhanced brain MRI images belonging to 38 categories. This work is accompanied by a paper found here http://arxiv.org/abs/2106.07333

This data set contains anonymised clinical MRI study, or a set of scans, of 515 patients with symptomatic back pains. Each patient data can have one or more MRI studies associated with it. Each study contains slices, i.e., individual images taken from either sagittal or axial view, of the lowest three vertebrae and the lowest three IVDs. The axial view slices are mainly taken from the last three IVDs – including the one between the last vertebrae and the sacrum. The orientation of the slices of the last IVD are made to follow the spine curve whereas those of the other IVDs are usually made in blocks – i.e., parallel to each other. There are between four to five slices per IVD and they begin from the top of the IVD towards its bottom. Many of the top and bottom slices cut through the vertebrae leaving between one to three slices that cut the IVD cleanly and show purely the image of that IVD. In most cases, the total number of slices in axial view ranges from 12 to 15. However, in some cases, there may be up to 20 slices because the study contains slices of more than last three vertebrae. The scans in sagittal view also vary but all contain at least the last seven vertebrae and the sacrum. While the number of vertebrae varies, each scan always includes the first two sacral links.

There are a total 48,345 MRI slices in our dataset. The majority of the slices have an image resolution of 320x320 pixels, however, there are slices from three studies with 320x310 pixel resolution. The pixels in all slices have 12-bit per pixel precision which is higher than the standard 8-bit greyscale images. Specifically for all axial-view slices, the slice thickness are uniformly 4 mm with centre-to-centre distance between adjacent slices to be 4.4 mm. The horizontal and vertical pixel spacing is 0.6875 mm uniformly across all axial-view slices.

The majority of the MRI studies were taken with the patient in Head-First-Supine position with the rests were taken with the patient in in Feet-First-Supine position. Each study can last between 15 to 45 minutes and a patient may have one or more study associated with them taken at a different time or a few days apart.

You can download and read the research papers detailing our methodology on boundary delineation for lumbar spinal stenosis detection using the URLs provided in the Related Links at the end of this page. You can also check out other dataset and source code related to this program from that section.

We kindly request you to cite our papers when using our data or program in your research.

Spine MRI Dataset, Fracture Detection, Anomaly Detection & Segmentation

The dataset consists of .dcm files containing MRI scans of the spine of the person with several dystrophic changes, such as osteochondrosis, spondyloarthrosis, hemangioma, physiological lordosis smoothed, osteophytes and aggravated defects. The images are labeled by the doctors and accompanied by report in PDF-format.

The dataset includes 9 studies, made from the different angles which provide a comprehensive understanding of a several dystrophic changes and useful in training spine anomaly classification algorithms. Each scan includes detailed imaging of the spine, including the vertebrae, discs, nerves, and surrounding tissues.

MRI study angles in the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F62acce9c1d60720bdd396e036718f406%2FFrame%2084.png?generation=1708543957118470&alt=media" alt="">

💴 For Commercial Usage: Full version of the dataset includes 20,000 spine studies of people with different conditions, leave a request on TrainingData to buy the dataset

Types of diseases and conditions in the full dataset:

• Degeneration of discs
• Osteophytes
• Osteochondrosis
• Hemangioma
• Disk extrusion
• Spondylitis
• AND MANY OTHER CONDITIONS

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fd2f21b9ac7dc26a3554e4647db47df57%2F3.gif?generation=1708543677763656&alt=media" alt="">

Researchers and healthcare professionals can use this dataset to study spinal conditions and disorders, such as herniated discs, spinal stenosis, scoliosis, and fractures. The dataset can also be used to develop and evaluate new imaging techniques, computer algorithms for image analysis, and artificial intelligence models for automated diagnosis.

OTHER MEDICAL SPINE MRI DATASETS:

• Spine MRI Dataset -Anomaly Detection
• Spine Segmentation Dataset - MRI Images
• Spinal Vertebrae Segmentation Dataset
• Spine Magnetic Resonance Imaging Dataset

💴 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 9 studies. Each study includes MRI-scans in .dcm and .jpg formats,
• DICOMDIR: includes information about the patient's condition and links to access files,
• Spine_MRI_2.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: visual, label, positive, negative, symptoms, clinically, sensory, varicella, syndrome, predictors, diagnosed, rsna cervical, image train, segmentations meta, spine train, mri spine scans, spinal imaging, radiology dataset, neuroimaging, medical imaging data, image segmentation, lumbar spine mri, thoracic spine mri, cervical spine mri, spine anatomy, spinal cord mri, orthopedic imaging, radiologist dataset, mri scan analysis, spine mri dataset, machine learning medical imaging, spinal abnormalities, image classification, neural network spine scans, mri data analysis, deep learning medical imaging, mri image processing, spine tumor detection, spine injury diagnosis, mri image segmentation, spine mri classification, artificial intelligence in radiology, spine abnormalities detection, spine pathology analysis, mri feature extraction, tomography, cloud

Shaip offers the best in class MRI scan Image Datasets for accurately training machine learning model. We offer MRI scan datasets for different body parts like brain, abdomen, breast, head, hip, knee, spin, and more.

Unidata’s Brain MRI dataset offers unique MRI scans and radiologist reports, aiding AI in detecting and diagnosing brain pathologies

This dataset contains 833 brain MRI images (T1w and T2w) from infancy and early childhood. The age of the subjects is between 0 months and 36 months. It contains a wide range of pathologies as well as healthy subjects. It is a quite diverse dataset acquired in the clinical routine over several years (images acquired with same scanner, but different protocols).

The T1w images are resampled to the shape of the T2w images. Then both are skull stripped.

All details about this dataset can be found in the paper "MyelinAge: Automated Estimation of Myelin Maturation on Brain MRI in Infancy and Early Childhood" (currently under review and link will be added). If you use this dataset please cite the paper.

The metadata can be found in the table meta.csv.

Description of columns:
myelinisation: myelin maturation status in terms of delayed, normal or accelerated according to evaluation by an expert radiologist. For more detail please see the paper.
age: the chronological age (in months) since birth.
age_corrected: the corrected chronological age (in months), which corrected for the premature babies by the number of month the baby was born before 37 weeks of gestation (in month), hence a preterm newborn gets a negative age.
doctor_predicted_age: the predicted age (in months) of the myelin maturation by expert radiologist (subjects with delayed myelin maturation will get lower values than their chronological age).
diagnosis: list of pathologies found in this dataset according to expert radiology reports.

In 616 MR images we segmented 50 anatomical structures covering a majority of relevant classes for most use cases. The MR images were randomly sampled from clinical routine, thus representing a real world dataset which generalizes to clinical application. The dataset contains a wide range of different pathologies, scanners, sequences and institutions. Moreover, it contains some images from IDC for further data diversity (see column "source" in meta.csv).

Link to a copy of this dataset on Dropbox for much quicker download: Dropbox Link

You can find a segmentation model trained on this dataset here.

More details about the dataset can be found in the corresponding paper. Please cite this paper if you use the dataset. The CT images described in the paper can be found here.

This dataset contains all 50 structures from the TotalSegmentator "total" task. It does not contain the structures of other TotalSegmentator MRI subtasks.

This dataset was created by the department of Research and Analysis at University Hospital Basel.

UPDATE: on 2025-01-21 we uploaded version 2.0.0 which increases the number of images from 298 to 616. It also contains slightly different structures.

Alzheimer_MRI Disease Classification Dataset

The Falah/Alzheimer_MRI Disease Classification dataset is a valuable resource for researchers and health medicine applications. This dataset focuses on the classification of Alzheimer's disease based on MRI scans. The dataset consists of brain MRI images labeled into four categories:

'0': Mild_Demented '1': Moderate_Demented '2': Non_Demented '3': Very_Mild_Demented

  Dataset Information

Train split:

Name: train Number of… See the full description on the dataset page: https://huggingface.co/datasets/Falah/Alzheimer_MRI.

This collection of prostate Magnetic Resonance Images (MRIs) was obtained with an endorectal and phased array surface coil at 3T (Philips Achieva). Each patient had biopsy confirmation of cancer and underwent a robotic-assisted radical prostatectomy. A mold was generated from each MRI, and the prostatectomy specimen was first placed in the mold, then cut in the same plane as the MRI. The data was generated at the National Cancer Institute, Bethesda, Maryland, USA between 2008-2010.

For scientific or other inquiries relating to this data set, please contact TCIA's Helpdesk.

The Bangladesh Brain Cancer MRI Dataset is a comprehensive collection of MRI images aimed at supporting research in medical diagnostics, particularly in the study of brain cancer. This dataset contains a total of 6056 images, systematically categorized into three distinct classes:

Brain_Glioma: 2004 images Brain_Menin: 2004 images Brain Tumor: 2048 images Each image in the dataset has been meticulously collected from various hospitals across Bangladesh, ensuring a diverse and representative sample. The images are uniformly resized to 512x512 pixels to facilitate compatibility with various image processing, machine learning, and deep learning algorithms.

The dataset is particularly valuable due to the rarity and difficulty in obtaining such medical imaging data, especially in the context of brain cancer. The collection process was made possible through the direct involvement of medical professionals, including experienced doctors who ensured the accuracy and relevance of the data. This collaboration underscores the dataset's potential utility in advancing modern medical science, offering a reliable resource for developing and testing diagnostic tools.

Researchers and practitioners can utilize this dataset for various applications, including but not limited to:

Image Processing: Enhancing and analyzing MRI images for better visualization and interpretation. Deep Learning: Training neural networks for automated classification and detection of brain cancer. Machine Learning: Developing predictive models to assist in early diagnosis and treatment planning. The dataset's focus on MRI images, a key diagnostic tool in oncology, makes it a crucial asset for anyone involved in the study or treatment of brain cancer.

Brain MRI image dataset - 2,000,000+ Studies

Dataset comprises 2,000,000+ medical studies featuring brain MRI scans paired with radiologists' reports, including detailed descriptions, conclusions, and recommendations. This large-scale dataset provides high-quality imaging data with 1 mm slice thickness and ≤5 mm interslice gap, averaging ~30 slices per scan. Designed for detection, classification, and segmentation tasks, it covers 50+ pathologies, including brain tumors, lesions… See the full description on the dataset page: https://huggingface.co/datasets/ud-medical/Brain-MRI-Dataset.

This Bangladeshi Brain Cancer MRI Dataset is a large dataset of Magnetic Resonance Imaging (MRI) images created to aid researchers in medical diagnosis, especially for brain cancer research. This collection contains a total of 1600 raw photos (every class have 400 raw images) after augmentation it contains total 6000 images, which are wisely divided into four main categories as:

Glioma -1500 images

Meningioma -1500 images

Pituitary-1500 images

No Tumor-1500 images

All the images in this dataset were collected from different hospitals around Bangladesh. It brought diversity and representation into the sample. To make the images compatible with various image processing, machine learning and deep-learning pipelines as possible they are then resized to a standardize size of 512×512.

This dataset is incredibly significant since high-quality data, such as medical imaging data, are few and difficult to obtain, particularly in the context of brain cancer. Assume that four prominent doctors collaborate on data collection in order to give more accurate and helpful content. It made it feasible. The cooperation emphasizes the dataset's potential to improve medical practice today by providing a dependable supply of diagnoses for use in diagnostic tool creation and testing within current medicine.

This dataset can be used by researchers and practitioners for a variety of applications such as Dense net 201, yolov8x/s, CNN, resnet50v2, VGG-16, MobilenetV2 etc.

Image Processing Details:

Images are randomly rotated within a range of 45 degrees. (rotation range=45)

Images are horizontally shifted by up to 20% of the width of the image. (width_shift_range=0.2)

Images are vertically shifted by up to 20% of the height of the image. (height_shift_range=0.2)

Shear transformation is applied to the image within a range of 20%. (shear range=0.2)

Images are randomly zoomed in or out by up to 20%. (zoom range=0.2)

Images are randomly flipped horizontally. (horizontal flip=True)

When transformations like rotations or shifts leave empty areas in the image, they are filled in by the nearest pixel values. (fill mode='nearest')

Hospital List(for Data Collection):

Ibn Sina Medical College, Kollanpur, 1, 1-B Mirpur Rd, Dhaka 1207

Dhaka Medical College & Hospital, Secretariat Rd, Dhaka 1000

Cumilla Medical College, Kuchaitoli, Dr. Akhtar Hameed Khan Road, Cumilla 3500, Bangladesh

Supervisor & investigator:

Md. Mizanur Rahman

Lecturer,

Computer Science and Engineering

Daffodil International University

Dhaka, Bangladesh

mizanurrahman.cse@diu.edu.bd

Data Collectors:

Md Shahriar Mannan Prottoy

Mahtab Chowdhury

Redwan Rahman

Azim Ullah Tamim

A dataset containing 100 T₂-weighted abdominal MRI scans and manually defined kidney masks. This MRI sequence is designed to optimise contrast between the kidneys and surrounding tissue to increase the accuracy of segmentation. Half of the acquisitions were acquired of healthy control subjects while the other half were acquired from Chronic Kidney Disease (CKD) patients. Ten of the subjects were scanned five times in the same session to enable assessment of the precision of Total Kidney Volume (TKV) measurements. More information about each subject can be found in the included csv file. This dataset was used to train a Convolutional Neural Network (CNN) to automatically segment the kidneys.

For more information about the dataset please refer to this article.

For an executable that allows automated segmentation of the kidneys from this dataset please refer to this software.

Experiment Details Participants watched Disney Pixar’s “Partly Cloudy” while lying in the scanner. There was no task; participants were simply instructed to lie still and watch the movie. The movie began after 10s of rest (black screen; TRs 1-5). The first 10s of the movie are the opening credits (disney castle, pixar logo; TRs 6-10).

IPS = 168 TRs TR = 2s Experiment length: 5.6 minutes

Pixar Movie Reverse Correlation Events Events defined by conducting reverse correlation analysis in two separate adult samples, using the average response in ToM brain regions (ToM events) and in the pain matrix (Pain events). Events listed are those that replicated across the two samples. Onsets and Durations are noted in TRs (1 TR = 2s); scanner trigger = TR 1.

Event types: Theory of Mind (ToM), Physical Sensation/Pain (Pain) TRs identified by RC analysis, as reported in Richardson et al. 2018 ToM Event Onsets; Durations 46; 2 52; 3 63; 2 91; 8 122; 3 129; 4 153; 3

Pain Event Onsets; Durations 38; 2 49; 1 56; 2 71; 5 100; 2 108; 6 117; 3 134; 3 159; 2

TIMING OF EVENTS FOR MODELING (taking into account hemodynamic lag (4s), scanner trigger = 0): All timings assume 10s from trigger until movie begins to play.

In seconds: Mental Event Onsets; Durations 86; 4 98; 6 120; 4 176; 16 238; 6 252; 8 300; 6

Pain Event Onsets; Durations 70; 4 92; 2 106; 4 136; 10 194; 4 210; 12 228; 6 262; 6 312; 4

In TRs when TR=2: Mental Event Onsets; Durations 43; 2 49; 3 60; 2 88; 8 119; 3 126; 4 150; 3

Pain Event Onsets; Durations 35; 2 46; 1 53; 2 68; 5 97; 2 105; 6 114; 3 131; 3 156; 2