The Brain/MINDS Marmoset MRI NA216 and eNA91 datasets currently constitutes the largest public marmoset brain MRI resource (483 individuals), and includes in vivo and ex vivo data for large variety of image modalities covering a wide age range of marmoset subjects.
* The in vivo part corresponds to a total of 455 individuals, ranging in age from 0.6 to 12.7 years (mean age: 3.86 ± 2.63), and standard brain data (NA216) from 216 of these individuals (mean age: 4.46 ± 2.62).
T1WI, T2WI, T1WI/T2WI, DTI metrics (FA, FAc, MD, RD, AD), DWI, rs-fMRI in awake and anesthetized states, NIfTI files (.nii.gz) of label data, individual brain and population average connectome matrix (structural and functional) csv files are included.
* The ex vivo part is ex vivo data, mainly from a subset of 91 individuals with a mean age of 5.27 ± 2.39 years.
It includes NIfTI files (.nii.gz) of standard brain, T2WI, DTI metrics (FA, FAc, MD, RD, AD), DWI, and label data, and csv files of individual brain and population average structural connectome matrices.

meningiomas

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 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

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.

This project has created a labeled MRI brain tumor dataset for the detection of three tumor types: pituitary, meningioma, and glioma. The dataset contains 2443 total images, which have been split into training, validation, and test sets. The training set has 1695 images, the validation set has 502 images, and the test set has 246 images.

Data: * Number of images: 2443 * Image types: MRI scans

Classes: * Pituitary tumor * Meningioma tumor * Glioma tumor * No Tumor

Split: * Training set: 1695 images * Validation set: 502 images * Test set: 246 images

Labeling: * The images have been labeled by medical experts using a standardized labeling protocol. * The labels include the type of tumor and the location of the tumor.

Potential Applications: * This dataset can be used to train machine learning models to automatically classify brain tumors. * The models could be used to assist radiologists in diagnosing brain tumors. * The dataset could also be used to develop new treatments for brain tumors.

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.

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

Spine MRI Dataset, Anomaly Detection & Segmentation

The dataset consists of .dcm files containing MRI scans of the spine of the person with several dystrophic changes, such as degeneration of discs, osteophytes, dorsal disk extrusion, spondylitis and asymmetry of B2 segments of vertebral arteries. The images are labeled by the doctors and accompanied by report in PDF-format. The dataset includes 5 studies, made from the different angles which provide a comprehensive understanding… See the full description on the dataset page: https://huggingface.co/datasets/TrainingDataPro/lumbar-spine-mri-dataset.

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

Data Source

https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection

  Dataset Card Authors

Mahadi Hassan

  Dataset Card Contact





  mahadise01@gmail.com





  Linkdin: https://www.linkedin.com/in/mahadise01





  Github: https://github.com/Mahadih534

Introduction

Magnetic Resonance Imaging (MRI) is widely recommended as a primary non-invasive diagnostic tool for endometriosis. Endometriomas affect 17–44% of women diagnosed with the condition. Accurate MRI-based ovary segmentation in endometriosis patients is essential for detecting endometriomas, guiding surgery, and predicting post-operative complications. However, ovary segmentation becomes challenging when the ovary is deformed or absent, often due to surgical resection, emphasizing the need for highly experienced clinicians. An automatic segmentation pipeline for pelvic MRI in endometriosis patients could greatly reduce the manual workload for clinicians and help standardize ovary segmentation.

The UTHealth Endometriosis MRI Dataset (UT-EndoMRI) includes multi-sequence MRI scans and structural labels collected from two clinical institutions, Memorial Hermann Hospital System and Texas Children’s Hospital Pavilion for Women. The first dataset comprises MRI scans and labels from 51 patients collected before 2022, featuring T2-weighted and T1-weighted fat-suppressed MRI sequences. The uterus, ovaries, endometriomas, cysts, and cul-de-sac structures were manually segmented by three raters. The second dataset, collected in 2022, consists of MRI scans and labels from 82 endometriosis patients. These sequences include T1-weighted, T1-weighted fat suppression, T2-weighted, and T2-weighted fat suppression MRI. In this dataset, the uterus, ovaries, and endometriomas were manually contoured by a single rater. Using these datasets, we investigated interrater agreement and developed an automatic ovary segmentation pipeline, RAovSeg, for endometriosis.

The study and the data sharing were approved by the Committee for the Protection of Human Subjects at UTHealth (protocol no. HSC-SBMI-22-0184). The UT-EndoMRI dataset is available for free use exclusively in non-commercial scientific research.

Endometriosis MRI

This dataset includes MRI scans and labels from two clinical institutions. The data from the first institution can be found in the ```D1_MHS/ ```directory, while the data from the second institution are located in the ```D2_TCPW/``` directory. Each subfolder contains MRI scans and corresponding labels from different raters.

The naming conventions for the files are as follows:

MRI scans:
D[dataset ID]- [patient ID] _ [MRI sequence].nii.gz

Anatomical structure labels:
D[dataset ID]- [patient ID] _ [structure name] _ r[rater ID].nii.gz

For the labels in the ```D2_TCPW/ ```directory, since they were generated by a single rater, there is no rater ID included in the file names.

The abbreviations used for naming:
T1: T1-weighted MRI
T1FS: T1-weighted fat suppression MRI
T2: T2-weighted MRI
T2FS: T2-weighted fat suppression MRI
ov: ovary
ut: uterus
em: endometrioma
cy: cyst
cds: cul de sac

For example, the file located at ```UT-EndoMRI/D1_MHS/D1-000/D1-000_T1FS.nii.gz```represents the T1 weighted fat suppression MRI for subject 000 in dataset 1. The file at ```UT-EndoMRI/D1_MHS/D1-000/D1-000_ ut_r1.nii.gz``` is the uterus segmentation manually contoured by rater 1 for subject 000 in dataset 1. The file at```UT-EndoMRI/ D2_TCPW/D2-006/D2-006_ cy.nii.gz``` is the cyst segmentation manually contoured for subject 006 in dataset 2.

MRI sequences may be missing due to a lack of acquisition.

Train/Validation/Test Replication

The data split for RAovSeg training, validation, and testing is provided as follows:
- Training/validation subjects IDs: D2-000 – D2-007
- Testing subjects IDs: D2-008 – D2-037
All data in dataset 1, as well as other data in dataset 2, are not used in RAovSeg development.

Data Acquisition

This dataset was acquired at the Texas Medical Center, within the Memorial Hermann Hospital System and the Texas Children’s Hospital Pavilion for Women. The study and the data sharing were approved by the Committee for the Protection of Human Subjects at UTHealth (protocol no. HSC-SBMI-22-0184).

User Agreement

The UT-EndoMRI dataset is available for free use exclusively in non-commercial scientific research. Any publications resulting from its use must cite the following paper.

X. Liang, L.A. Alpuing Radilla, K. Khalaj, H. Dawoodally, C. Mokashi, X. Guan, K.E. Roberts, S.A. Sheth, V.S. Tammisetti, L. Giancardo. "A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis." (submitted)

Funding

This work has been supported by the Robert and Janice McNair Foundation.

Research Team

Here are the people behind this data acquisition effort:
Xiaomin Liang, Linda A Alpuing Radilla, Kamand Khalaj, Haaniya Dawoodally, Chinmay Mokashi, Xiaoming Guan, Kirk E Roberts, Sunil A Sheth, Varaha S Tammisetti, Luca Giancardo

Acknowledgements

We would also like to acknowledge for their support: Memorial Hermann Hospital System and Texas Children’s Hospital Pavilion for Women.

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 "Development and Evaluation of Deep Learning Models for Automated Estimation of Myelin Maturation Using Pediatric Brain MRI Scans". If you use this dataset please cite our paper: https://pubs.rsna.org/doi/10.1148/ryai.220292

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.

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 PM MRI Dataset is a comprehensive collection of MRI scans designed for post-mortem (PM) medical imaging research. This dataset provides high-resolution scans for advanced detection, classification, and segmentation tasks, making it an invaluable resource for forensic, neuropathological, and AI/ML applications in medical imaging.

💾 Access the Dataset This is a limited preview of the data. To access the full dataset, please visit HumanAIzeDATA to discuss your requirements and pricing options.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F24887857%2Fe267a19bb60a63a23a9d087b917679d3%2FCapture%20dcran%202025-02-17%20175733.png?generation=1739814250558481&alt=media" alt="">

📂 Dataset Content

The dataset includes:

• High-quality post-mortem MRI scans
• Multiple imaging sequences for detailed analysis
• DICOM (DCM) format for compatibility with medical imaging tools

Medical reports provide the following details:

• Type of study
• MRI machine specifications
• Patient demographics: Age, sex, race

🌐 About HumanAIzeDATA

HumanAIzeDATA specializes in high-quality datasets, content moderation, data collection, and annotation for AI/ML projects.

For more details, visit our website: HumanAIzeDATA

The MCND dataset incorporates MRI data from three neurological disorders, released on the Kaggle repository. These include Alzheimer’s Disease (AD) [1], Brain Tumor (BT) [2], and Multiple Sclerosis (MS) [3]. This dataset contains 16400 images of human brain MRI images which are classified into 8 classes: AD-MildDemented, AD-ModerateDemented, AD-VeryMildDemented, BT-glioma, BT-meningioma, BT-pituitary, MS, and Normal (healthy).

1. https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images
2. https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset/data
3. https://www.kaggle.com/datasets/buraktaci/multiple-sclerosis

1) Data Introduction • The Brain Tumor MRI Dataset is a collection of Magnetic Resonance Imaging (MRI) images curated for the classification of brain tumors. The dataset consists of MRI scans categorized into four classes: glioma, meningioma, pituitary tumor, and no tumor.

2) Data Utilization (1) Characteristics of the Brain Tumor MRI Dataset: • This dataset has been constructed as training data for artificial intelligence (AI) models aimed at the early detection and precise classification of brain tumors. It helps improve the accuracy and efficiency of medical diagnoses. • Each image is labeled with the tumor type, making the dataset well-suited for multiclass classification tasks.

(2) Applications of the Brain Tumor MRI Dataset: • Development of tumor classification models: The dataset can be used to develop AI systems that automatically classify the type of brain tumor. • Detection of tumor location and boundaries: The dataset can be utilized to train models that not only detect the presence of a tumor but also identify its location and size, contributing to effective pre-surgical planning.

This dataset from Taipei Veterans General Hospital represents the most comprehensive collection of brain metastases MRI data in the country. It includes axial SE T1WI+C images annotated according to the standard DICOM-RT structure set (RTSS) format, used in both clinical evaluations and Gamma Knife treatments of intracranial metastatic tumors. Each case in the dataset comprises T1-weighted images (T1W), T2-weighted images (T2W), and T1 post-contrast weighted images (T1W+C). The dataset is notable for the high number of cases and the exceptional completeness and precision of the annotations, making it one of the most extensive and detailed brain metastases datasets globally. This allows for a diverse range of AI research projects.

GSP