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

Brain Cancer - MRI Dataset

This dataset contains a comprehensive collection of MRI images for brain cancer research, specifically aimed at supporting medical diagnostics.

Categories

• Medical Education
• Brain Cancer
• Machine Learning
• Image Classification
• Brain Areas
• Deep Learning

Contributor

Md Mizanur Rahman

Publication Date

August 5, 2024

DOI

10.17632/mk56jw9rns.1

Description

The Bangladesh Brain Cancer MRI Dataset is a comprehensive collection of MRI images categorized into three distinct classes:

• Brain_Glioma: 2004 images
• Brain_Menin: 2004 images
• Brain Tumor: 2048 images

The dataset includes a total of 6056 images, uniformly resized to 512x512 pixels. These images were collected from various hospitals across Bangladesh with the direct involvement of experienced medical professionals to ensure accuracy and relevance. This dataset is valuable due to the difficulty in obtaining such medical imaging data and offers a reliable resource for developing and testing diagnostic tools.

Potential Applications

Researchers and practitioners can utilize this dataset for various applications, including:

• Image Processing: Enhancing and analyzing MRI images.
• Deep Learning: Training neural networks for automated classification and detection of brain cancer.
• Machine Learning: Developing predictive models for early diagnosis and treatment planning.

Citation

Rahman, Md Mizanur (2024), “Brain Cancer - MRI dataset”, Mendeley Data, V1, doi: 10.17632/mk56jw9rns.1

Please upvote if you find this dataset useful!

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

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

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.

Brain Tumor Detection Dataset

Overview

This dataset contains high-quality MRI images of brain tumors with detailed annotations. The dataset is meticulously curated, cleaned, and annotated to aid in the development and evaluation of machine learning models for brain tumor detection and classification.

Dataset Composition

The dataset includes a total of 5,249 MRI images divided into training and validation sets. Each image is annotated with bounding boxes in YOLO format, and labels corresponding to one of the four classes of brain tumors.

Classes

• Class 0: Glioma
• Class 1: Meningioma
• Class 2: No Tumor
• Class 3: Pituitary

Data Split

1. Training Set:

• Glioma: 1,153 images
• Meningioma: 1,449 images
• No Tumor: 711 images
• Pituitary: 1,424 images

2. Validation Set:

• Glioma: 136 images
• Meningioma: 140 images
• No Tumor: 100 images
• Pituitary: 136 images

Image Characteristics

The images in the dataset are from different angles of MRI scans including sagittal, axial, and coronal views. This variety ensures comprehensive coverage of brain anatomy, enhancing the robustness of models trained on this dataset.

Annotations

The bounding boxes were manually annotated using the LabelImg tool by a dedicated team. This rigorous process ensures high accuracy and reliability of the annotations.

Source and Inspiration

This dataset was inspired by two existing datasets: 1. https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset 2. https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri

A thorough cleaning process was performed to remove noisy, mislabeled, and poor-quality images, resulting in a high-quality and well-labeled dataset.

Usage

This dataset is suitable for training and validating deep learning models for the detection and classification of brain tumors. The variety in MRI scan angles and the precision of annotations provide an excellent foundation for developing robust computer vision applications in medical imaging.

Citation

If you use this dataset in your research or project, please consider citing it appropriately to acknowledge the effort put into its creation and annotation.

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.

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.

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

Dataset Card for "Brain-Tumor-MRI-Dataset"

More Information needed

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 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.

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.

personal 101 MRI images, 24-year-old male, dimensions: 255x255, sagittal orientation

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.

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 dataset was derived from tracked biopsy sessions using the Artemis biopsy system, many of which included image fusion with MRI targets. Patients received a 3D transrectal ultrasound scan, after which nonrigid registration (e.g. “fusion”) was performed between real-time ultrasound and preoperative MRI, enabling biopsy cores to be sampled from MR regions of interest. Most cases also included sampling of systematic biopsy cores using a 12-core digital template. The Artemis system tracked targeted and systematic core locations using encoder kinematics of a mechanical arm, and recorded locations relative to the Ultrasound scan. MRI biopsy coordinates were also recorded for most cases. STL files and biopsy overlays are available and can be visualized in 3D Slicer with the SlicerHeart extension. Spreadsheets summarizing biopsy and MR target data are also available. See the Detailed Description tab below for more information.

MRI targets were defined using multiparametric MRI, e.g. t2-weighted, diffusion-weighted, and perfusion-weighted sequences, and scored on a Likert-like scale with close correspondence to PIRADS version 2. t2-weighted MRI was used to trace ROI contours, and is the only sequence provided in this dataset. MR imaging was performed on a 3 Tesla Trio, Verio or Skyra scanner (Siemens, Erlangen, Germany). A transabdominal phased array was used in all cases, and an endorectal coil was used in a subset of cases. The majority of pulse sequences are 3D T2:SPC, with TR/TE 2200/203, Matrix/FOV 256 × 205/14 × 14 cm, and 1.5mm slice spacing. Some cases were instead 3D T2:TSE with TR/TE 3800–5040/101, and a small minority were imported from other institutions (various T2 protocols.)

Ultrasound scans were performed with Hitachi Hi-Vision 5500 7.5 MHz or the Noblus C41V 2-10 MHz end-fire probe. 3D scans were acquired by rotation of the end-fire probe 200 degrees about its axis, and interpolating to resample the volume with isotropic resolution.

Patients with suspicion of prostate cancer due to elevated PSA and/or suspicious imaging findings were consecutively accrued. Any consented patient who underwent or had planned to receive a routine, standard-of-care prostate biopsy at the UCLA Clark Urology Center was included.

Note: Some Private Tags in this collection are critical to properly displaying the STL surface and the Prostate anatomy. Private Tag (1129,"Eigen, Inc",1016) DS VoxelSize is especially important for multi-frame US cases.

Unidata Spine MRI dataset provides comprehensive spinal scans, improving AI’s ability to detect and diagnose spinal conditions