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

meningiomas

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

The data consists of MRI images. The data has four classes of images both in training as well as a testing set:

1. Mild Demented
2. Moderate Demented
3. Non Demented
4. Very Mild Demented

The data contains two folders. One of them is augmented ones and the other one is originals. Originals could be used for validation or test dataset...

Data is augmented from an existing dataset. Original images can be seen in Data Explorer. https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images

My purpose of the publish this dataset is to the usage of augmented images as well as originals. The importance of augmentation is can be a little underrated.

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





  💴 For Commercial Usage: Full version of the dataset includes… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/brain-mri-dataset.

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.

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

This dataset contains a collection of multimodal medical images, specifically CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) scans, for brain tumor detection and analysis. It is designed to assist researchers and healthcare professionals in developing AI models for the automatic detection, classification, and segmentation of brain tumors. The dataset features images from both modalities, providing comprehensive insight into the structural and functional variations in the brain associated with various types of tumors.

The dataset includes high-resolution CT and MRI images captured from multiple patients, with each image labeled with the corresponding tumor type (e.g., glioma, meningioma, etc.) and its location within the brain. This combination of CT and MRI images aims to leverage the strengths of both imaging techniques: CT scans for clear bone structure visualization and MRI for soft tissue details, enabling a more accurate analysis of brain tumors.

I collected these data from different sources and modified data for maximum accuracy.

Brain Tumor CT scan Images source

1. CT Brain Segmentation Computer Vision Project -- https://universe.roboflow.com/joshua-zgc7b/ct-brain-segmentation
2. CT and MRI brain scans -- https://www.kaggle.com/datasets/darren2020/ct-to-mri-cgan
3. CT Head Scans(jpg files) -- https://www.kaggle.com/datasets/clarksaben/ct-head-scans
4. Head CT Images for Classification -- https://www.kaggle.com/datasets/nipaanjum/head-ct-images-for-classification
5. Anonymous brain -- from private data
6. Unpaired MR-CT Brain Dataset for Unsupervised Image Translation -- https://data.mendeley.com/datasets/z4wc364g79/1

Brain Tumor MRI images source

1. Brain Tumor (MRI Scans) -- https://www.kaggle.com/datasets/rm1000/brain-tumor-mri-scans
2. Brain Tumor MRIs -- https://www.kaggle.com/datasets/vinayjayanti/brain-tumor-mris
3. Siardataset -- https://www.kaggle.com/datasets/masoumehsiar/siardataset
4. Brain tumors 256x256 -- https://www.kaggle.com/datasets/thomasdubail/brain-tumors-256x256
5. Brain Tumor MRI Image Classification Dataset -- https://www.kaggle.com/datasets/iashiqul/brain-tumor-mri-image-classification-dataset
6. Brain Tumor MRI (yes or no) -- https://www.kaggle.com/datasets/mohamada2274/brain-tumor-mri-yes-or-no
7. BRAIN TUMOR CLASS CLASS Computer Vision Project -- https://universe.roboflow.com/college-sf5ih/brain-tumor-class-class
8. Brain Tumor Detection Computer Vision Project -- https://universe.roboflow.com/tuan-nur-afrina-zahira/brain-tumor-detection-bmmqz
9. Tumor Detection Computer Vision Project -- https://universe.roboflow.com/brain-tumor-detection-wsera/tumor-detection-ko5jp

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.

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.

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.

GSP

Introduction

MRI-based artificial intelligence (AI) research on patients with brain gliomas has been rapidly increasing in popularity in recent years in part due to a growing number of publicly available MRI datasets. Notable examples include The Cancer Genome Atlas Glioblastoma dataset (TCGA-GBM) consisting of 262 subjects and the International Brain Tumor Segmentation (BraTS) challenge dataset consisting of 542 subjects (including 243 preoperative cases from TCGA-GBM). The public availability of these glioma MRI datasets has fostered the growth of numerous emerging AI techniques including automated tumor segmentation, radiogenomics, and MRI-based survival prediction. Despite these advances, existing publicly available glioma MRI datasets have been largely limited to only 4 MRI contrasts (T2, T2/FLAIR, and T1 pre- and post-contrast) and imaging protocols vary significantly in terms of magnetic field strength and acquisition parameters. Here we present the University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) dataset. The UCSF-PDGM dataset includes 501 subjects with histopathologically-proven diffuse gliomas who were imaged with a standardized 3 Tesla preoperative brain tumor MRI protocol featuring predominantly 3D imaging, as well as advanced diffusion and perfusion imaging techniques. The dataset also includes isocitrate dehydrogenase (IDH) mutation status for all cases and O[6]-methylguanine-DNA methyltransferase (MGMT) promotor methylation status for World Health Organization (WHO) grade III and IV gliomas. The UCSF-PDGM has been made publicly available in the hopes that researchers around the world will use these data to continue to push the boundaries of AI applications for diffuse gliomas.

Methods

Patient Population

Data collection was performed in accordance with relevant guidelines and regulations and was approved by the University of California San Francisco institutional review board with a waiver for consent. The dataset population consisted of 501* adult patients with histopathologically confirmed grade II-IV diffuse gliomas who underwent preoperative MRI, initial tumor resection, and tumor genetic testing at a single medical center between 2015 and 2021. Patients with any prior history of brain tumor treatment were excluded; however, history of tumor biopsy was not considered an exclusion criterion.

Genetic Biomarker Testing

All subjects’ tumors were tested for IDH mutations by genetic sequencing of tissue acquired during biopsy or resection. All grade III and IV tumors were tested for MGMT methylation status using a methylation sensitive quantitative PCR assay.

Study participant demographic data

The 501* cases included in the UCSF-PDGM include 55 (11%) grade II, 42 (9%) grade III, and 403 (80%) grade IV tumors. There was a male predominance for all tumor grades (56%, 60%, and 60%, respectively for grades II-IV). IDH mutations were identified in a majority of grade II (83%) and grade III (67%) tumors and a small minority of grade IV tumors (8%). MGMT promoter hypermethylation was detected in 63% of grade IV gliomas and was not tested for in a majority of lower grade gliomas. 1p/19q codeletion was detected in 20% of grade II tumors and a small minority of grade III (5%) and IV (<1%) tumors. Tabulated details and glossary are available in the Data Access and Detailed Description tabs below.

Image Acquisition

All preoperative MRI was performed on a 3.0 tesla scanner (Discovery 750, GE Healthcare, Waukesha, Wisconsin, USA) and a dedicated 8-channel head coil (Invivo, Gainesville, Florida, USA). The imaging protocol included 3D T2-weighted, T2/FLAIR-weighted, susceptibility-weighted (SWI), diffusion-weighted (DWI), pre- and post-contrast T1-weighted images, 3D arterial spin labeling (ASL) perfusion images, and 2D 55-direction high angular resolution diffusion imaging (HARDI). Over the study period, two gadolinium-based contrast agents were used: gadobutrol (Gadovist, Bayer, LOC) at a dose of 0.1 mL/kg and gadoterate (Dotarem, Guerbet, Aulnay-sous-Bois, France) at a dose of 0.2 mL/kg.

Image Pre-Processing

HARDI data were eddy current corrected and processed using the Eddy and DTIFIT modules from FSL 6.0.2 yielding isotropic diffusion weighted images (DWI) and several quantitative diffusivity maps: mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and fractional anisotropy (FA). Eddy correction was performed with outlier replacement on and topup correction off. DTIFIT was performed with simple least squares regression. Each image contrast was registered and resampled to the 3D space defined by the T2/FLAIR image (1 mm isotropic resolution) using automated non-linear registration (Advanced Normalization Tools). Resampled co-registered data were then skull stripped using a previously described and publicly available deep-learning algorithm: https://www.github.com/ecalabr/brain_mask/.

Tumor Segmentation

Multicompartment tumor segmentation of study data was undertaken as part of the 2021 BraTS challenge. Briefly, image data first underwent automated segmentation using an ensemble model consisting of prior BraTS challenge winning segmentation algorithms. Images were then manually corrected by trained radiologists and approved by 2 expert reviewers. Segmentation included three major tumor compartments: enhancing tumor, non-enhancing/necrotic tumor, and surrounding FLAIR abnormality (sometimes referred to as edema).

The UCSF-PDGM adds to on an existing body of publicly available diffuse glioma MRI datasets that are commonly used in AI research applications. As MRI-based AI research applications continue to grow, new data are needed to foster development of new techniques and increase the generalizability of existing algorithms. The UCSF-PDGM not only significantly increases the total number of publicly available diffuse glioma MRI cases, but also provides a unique contribution in terms of MRI technique. The inclusion of 3D sequences and advanced MRI techniques like ASL and HARDI provides a new opportunity for researchers to explore the potential utility of cutting-edge clinical diagnostics for AI applications. In addition, these advanced imaging techniques may prove useful for radiogenomic studies focused on identification of IDH mutations or MGMT promoter methylation.

The UCSF-PDGM dataset, particularly when combined with existing publicly available datasets, has the potential to fuel the next phase of radiologic AI research on diffuse gliomas. However, the UCSF-PDGM dataset’s potential will only be realized if the radiology AI research community takes advantage of this new data resource. We hope that this dataset sparks inspiration in the next generation of AI researchers, and we look forward to the new techniques and discoveries that the UCSF-PDGM will generate.

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.

Averaged T1-weighted MPRAGE with an isotropic resolution of 250 µmThis folder contains structural data of a single young healthy Caucasian subject in NIfTI file format. This dataset has been build by registering eight single T1-weighted MPRAGE volumes with a native isotropic resolution of 250 µm and the average to increase the SNR.MPRAGE_250um.tarAll averages of T1-weighted MPRAGE with an isotropic resolution of 250 µmThis folder contains all prior averages of the registration process to build the final T1-weighted MPRAGE volume with an isotropic resolution of 250 µm.averages.tarPre-Processed T1-weighted MPRAGE volumesThis folder contains all eight single pre-processed T1-weighted MPRAGE volumes used to generate the average with an isotropic resolution of 250 µm. Additionally, 1 and 0.5 mm pre-processed data of the same subject are included in the folder. Pre-processing consists of AC-PC alignment and bias field correction. Details can be found in the readme.derivatives.tarSourcedata of...

The respective data is comprised of 5 different datasets of medical images collected by the contributors, which can be used for classifying Lung Cancer, Bone Fracture, Brain tumor, Skin Lesions, and Renal Malignancy, respectively. The data also includes multiple disease and malignancy images for the respective dataset. The classification for the diseases can be done by using ResNet50 CNN architecture and other DCNN models. This data is also been used in a research article by the contributor.

FOMO-60K: Brain MRI Dataset for Large-Scale Self-Supervised Learning with Clinical Data

Dataset paper preprint: A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning. https://arxiv.org/pdf/2506.14432. This repo contains FOMO-60K, a large-scale dataset which includes MRIs from the clinic, containing 60K+ brain MRI scans. This dataset is released in conjunction with the FOMO25: Foundation Model Challenge for Brain MRI hosted at MICCAI… See the full description on the dataset page: https://huggingface.co/datasets/FOMO25/FOMO-MRI.

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.

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.