LGG Segmentation DatasetThis dataset contains brain MR images together with manual FLAIR abnormality segmentation masks.The images were obtained from The Cancer Imaging Archive (TCIA).They correspond to 110 patients included in The Cancer Genome Atlas (TCGA) lower-grade glioma collection with at least fluid-attenuated inversion recovery (FLAIR) sequence and genomic cluster data available.Tumor genomic clusters and patient data is provided in data.csv file.

Description This dataset is a comprehensive collection of Brain Magnetic Resonance Imaging (MRI) scans, meticulously annotated with the Segment Anything Model (SAM). The data is stored in a CSV file format for easy access and manipulation.

Content The dataset contains MRI scans of the brain, each of which is annotated with SAM. The annotations provide detailed information about the segmentation of various structures present in brain scans. The dataset is designed to aid in developing and validating algorithms for automatic brain structure segmentation.

T2-weighted Kidney MRI Segmentation Dataset

A High-Quality Dataset for Kidney Segmentation in MRI

📌 Overview

This dataset contains 100 T2-weighted abdominal MRI scans with manually segmented kidney masks. The MRI sequence is optimized to enhance the contrast between the kidneys and surrounding tissues, improving segmentation accuracy. It includes scans from: - ✅ Healthy control subjects - ✅ Chronic Kidney Disease (CKD) patients

Additionally, 10 subjects were scanned five times in a single session to assess the precision of Total Kidney Volume (TKV) measurements.

📊 Dataset Details

• Total MRI Scans: 100
• Scanned Groups: Healthy & CKD Patients
• Repetitive Scans for TKV Analysis: 10 subjects (scanned 5 times each)
• Data Format: NIfTI (.nii)
• Additional Data: Subject information in CSV file
• Size: 160.3 MB
• Source: UK Renal Imaging Network (UKRIN)

🔍 Use Cases

• ✔️ Deep Learning & AI-based Kidney Segmentation
• ✔️ Chronic Kidney Disease (CKD) Analysis
• ✔️ Medical Imaging Research in MRI
• ✔️ Automated Organ Segmentation Models

🎈**Description**

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.

🔬 Research Potential

This dataset is valuable for AI researchers, radiologists, and biomedical engineers developing CNN-based kidney segmentation models. It has been used in deep learning applications for renal segmentation and can support advancements in functional kidney imaging and CKD research.

⚖️ License

CC BY 4.0 (Free to use for research and commercial applications with proper attribution)

📢 Citation

If you use this dataset, please cite: Daniel, A. J., Buchanan, C. E., Allcock, T., Scerri, D., Cox, E. F., Prestwich, B. L., & Francis, S. T. (2021). T2-weighted Kidney MRI Segmentation (v1.0.0) [Data set]. Zenodo. DOI: 10.5281/zenodo.5153568

ℹ More information

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

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

Brain Mri Segmentation

## Overview

Brain Mri Segmentation is a dataset for object detection tasks - it contains Brain annotations for 223 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Spine MRI Dataset, Fracture Detection & Segmentation

The dataset is created on the basis of Spine MRI Dataset

The dataset consists of .dcm files containing MRI scans of the spine of the person with several dystrophic changes, such as osteophytes, dorsal disc extrusions, dorsal disc protrusions and spondyloarthrosis. 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 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

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💴 For Commercial Usage: Full version of the dataset includes 20,000 spine studies of people with different conditions, leave a request on our website 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%2F9fc2e49ddc55f0095eca9104f59ee768%2F2.gif?generation=1708950966715941&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
• Spinal Cord Images - Spine MRI Dataset
• Spinal Vertebrae Segmentation Dataset
• Spine Magnetic Resonance Imaging Dataset

🧩 This is just an example of the data. Leave a request here to learn more

Content

The dataset includes:

• ST000001: includes subfolders with 5 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_3.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.

🚀 You can learn more about our high-quality unique datasets here

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This is a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 studies of 218 patients with a history of low back pain. The data was collected from four different hospitals. There is an additional hidden test set, not available here, used in the accompanying SPIDER challenge on spider.grand-challenge.org. We share this data to encourage wider participation and collaboration in the field of spine segmentation, and ultimately improve the diagnostic value of lumbar spine MRI.

Which MRI studies are assigned to the training and validation sets can be found in the overview file. This file also provides the biological sex for all patients and the age for the patients for which this was available. It also includes a number of scanner and acquisition parameters for each individual MRI study. The dataset also comes with radiological gradings found in a separate file for the following degenerative changes:

1.    Modic changes (type I, II or III)

2.    Upper and lower endplate changes / Schmorl nodes (binary)

3.    Spondylolisthesis (binary)

4.    Disc herniation (binary)

5.    Disc narrowing (binary)

6.    Disc bulging (binary)

7.    Pfirrman grade (grade 1 to 5).

All radiological gradings are provided per IVD level.

Brain Tumor MRI Segmentation

## Overview

Brain Tumor MRI Segmentation is a dataset for instance segmentation tasks - it contains Tumors annotations for 669 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Three-dimensional cine-MRI dataset for cardiac segmentation and classification

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.

Dataset obtained from Open Neuro. 15 cases with segmentation of side ventricles on brain MRI T1W scans. Cases obtained from open neuro: https://openneuro.org/ Segmentations done by the MedSeg team Our site: MedSeg Our tool: MedSeg Segmentation More data here

Brain tumour MRI segmentation dataset

Dataset

This dataset was created by Varun Raskar

Released under MIT

Contents

This dataset consists of manually segmented 2D static upper airway images acquired at the University of Iowa 3T research scanner. The images were captured using a fast GRE sequence in the midsagittal plane, with a spatial resolution of 2.7 mm² and approximately 6 frames per second, and a field of view (FOV) of 20x20 cm².The airways in 1000 image frames from 5 volunteers were manually segmented while performing various speech tasks, such as producing za-na-za, loo-lee-la, apa-ipi-upu, counting numbers, and speaking spontaneous speech. This dataset is suitable for training a deep learning model to segment the upper airway.The dataset is structured as follows:Image: Zip folder containing mid-sagittal images of a dynamic airway (256 x 256 pixels).Mask: Zip folder containing the respective segmentations for the images.If you use this dataset, please cite the following papers:1. [ERATTAKULANGARA, SUBIN, et al. "Stacked hybrid learning U-NET for segmentation of multiple articulators in speech MRI." ISMRM 2021]2. [Erattakulangara, Subin, et al. "Automatic Multiple Articulator Segmentation in Dynamic Speech MRI Using a Protocol Adaptive Stacked Transfer Learning U-NET Model." Bioengineering 10.5 (2023): 623.]

BRISC 2025 — Brain Tumor MRI DatasetBRISC (BRain tumor Image Segmentation & Classification) — a curated, expert-annotated T1 MRI dataset for multi-class brain tumor classification and pixel-wise segmentation.ArXiv preprint (Fateh et al., 2025): https://arxiv.org/abs/2506.14318🚀 OverviewBRISC is designed to address common shortcomings in existing public brain MRI collections (e.g., class imbalance, limited tumor types, and annotation inconsistency). It provides high-quality, physician-validated pixel-level masks and a balanced multi-class classification split, suitable for benchmarking segmentation and classification algorithms as well as multi-task learning research.Highlights- 6,000 T1-weighted MRI slices (5,000 train / 1,000 test)- Four classes: Glioma, Meningioma, Pituitary Tumor, No Tumor- Pixel-wise segmentation masks reviewed by radiologists- Slices from three anatomical planes: Axial, Coronal, Sagittal- Clean, stratified train/test splits and aligned image–mask filenames📦 Dataset structurebrisc2025/├─ classification_task/│ ├─ train/│ │ ├─ glioma/│ │ │ ├─ brisc2025_train_00001_gl_ax_t1.jpg│ │ │ └─ ...│ │ ├─ meningioma/│ │ ├─ pituitary/│ │ └─ no_tumor/│ └─ test/│ ├─ glioma/│ │ ├─ brisc2025_test_00001_gl_ax_t1.jpg│ │ └─ ...│ ├─ meningioma/│ ├─ pituitary/│ └─ no_tumor/├─ segmentation_task/│ ├─ train/│ │ ├─ images/│ │ │ ├─ brisc2025_train_00001_gl_ax_t1.jpg│ │ │ └─ ...│ │ └─ masks/│ │ ├─ brisc2025_train_00001_gl_ax_t1.png│ │ └─ ...│ └─ test/│ ├─ images/│ │ ├─ brisc2025_test_00001_gl_ax_t1.jpg│ │ └─ ...│ └─ masks/│ ├─ brisc2025_test_00001_gl_ax_t1.png│ └─ ...├─ manifest.json├─ manifest.csv├─ manifest.json.sha256├─ manifest.csv.sha256└─ README.mdNotes:- Classification folders contain image-level labels suitable for standard image classification pipelines.- Segmentation folders contain paired MRI images/ and corresponding binary masks/.- Image and mask filenames are identical except for file extension (images: .jpg, masks: .png).- All images are T1-weighted slices.📊 Dataset statistics- Total samples: 6,000 (5,000 train / 1,000 test)- Classes: 4 (balanced distribution across train/test)- Planes: Axial / Coronal / Sagittal (balanced representation)- Imaging modality: T1-weighted MRI- Annotation quality: Reviewed and corrected by medical experts📄 CitationIf you use BRISC in your work, please cite:@article{fateh2025brisc,title={Brisc: Annotated dataset for brain tumor segmentation and classification with swin-hafnet},author={Fateh, Amirreza and Rezvani, Yasin and Moayedi, Sara and Rezvani, Sadjad and Fateh, Fatemeh and Fateh, Mansoor and Abolghasemi, Vahid},journal={arXiv preprint arXiv:2506.14318},year={2025}}🤝 AcknowledgmentsThanks to the collaborating radiologists and physicians for expert annotation and review.🔗 References & inspirationsThis dataset drew design and organizational inspiration from widely used brain tumor imaging datasets (e.g., BraTS, Figshare datasets, Kaggle collections). See the project paper for full details and evaluation results.

Brain Tumor Segmentation

## Overview

Brain Tumor Segmentation is a dataset for instance segmentation tasks - it contains Brain Tumors annotations for 339 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This dataset contains T1-weighted MR images of 50 subjects, 40 of whom are patients with temporal lobe epilepsy and 10 are nonepileptic subjects. Hippocampus labels are provided for 25 subjects for training. The users may submit their segmentation outcomes for the remaining 25 testing images to get a table of segmentation metrics. HFH ├── ReadMe.txt ├── Test │ ├── HFH_026.hdr │ ├── HFH_026.img │ ├── HFH_027.hdr │ ├── HFH_027.img │ ├── HFH_028.hdr │ ├── HFH_028.img │ ├── HFH_029.hdr │ ├── HFH_029.img │ ├── HFH_030.hdr │ ├── HFH_030.img │ ├── HFH_031.hdr │ ├── HFH_031.img │ ├── HFH_032.hdr │ ├── HFH_032.img │ ├── HFH_033.hdr │ ├── HFH_033.img │ ├── HFH_034.hdr │ ├── HFH_034.img │ ├── H

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.

Integrated MRI and linear accelerator systems (MR-Linacs) provide superior soft tissue contrast and the capability of adapting radiotherapy plans to changes in daily anatomy. In this dataset, serial MRIs of the abdomen of patients undergoing radiotherapy were collected and the luminal gastro-intestinal tract was segmented to develop a deep learning algorithm for automatic segmentation. This dataset was used by UW-Madison GI Tract Image Segmentation challenge hosted at Kaggle. This release includes both the training and test sets in the Kaggle challenge. We anticipate that the data may be utilized by radiation oncologists, medical physicists, and data scientist to further improve MRI segmentation algorithms.

If you find our dataset useful, please consider citing our paper.

Lee, S. L., Yadav, P., Li, Y., Meudt, J. J., Strang, J., Hebel, D., ... & Bassetti, M. F. (2024). Dataset for gastrointestinal tract segmentation on serial MRIs for abdominal tumor radiotherapy. Data in Brief, 57, 111159.

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 presents a collection of MRI scans from 277 patients diagnosed with primary Nasopharyngeal Carcinoma (NPC), accompanied by radiologists' annotated segmentations. Each scan includes T1-weighted, T2-weighted, and contrast-enhanced T1-weighted axial slices, providing detailed insights into tumor morphology. The annotations precisely delineate gross tumor volume and boundaries. The contemporary clinical and laboratory diagnoses with follow-up survival are contained in the dataset.

Collected with ethical approval, all data is anonymized to ensure privacy and confidentiality. For questions please contact Yin Li.

Please cite this work as follows:
@misc{li2024dataset,
title={A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation},
author={Yin Li and Qi Chen and Kai Wang and Meige Li and Liping Si and Yingwei Guo and Yu Xiong and Qixing Wang and Yang Qin and Ling Xu and Patrick van der Smagt and Jun Tang and Nutan Chen},
year={2024},
eprint={2404.03253},
archivePrefix={arXiv},
primaryClass={eess.IV}
}