Spine Magnetic Resonance Imaging Dataset

Spine Magnetic Resonance Imaging Dataset


Spine Magnetic Resonance Imaging Dataset


Spine Magnetic Resonance Imaging Dataset

Use Case

Computer Vision


The dataset consists of .dcm files containing MRI scans of the spine of the person with several dystrophic changes

Spine Magnetic Resonance Imaging Dataset

About Dataset

Spine MRI Dataset, Fracture Detection & Segmentation

This dataset contains MRI scans of the spine showing various abnormalities that can occur, like changes in spine shape, bone growths, bulging discs, lesions within the spinal cord, fluid-filled cavities, and issues with the spinal canal and vertebral arteries. The scans are saved as .dcm files and come with reports in PDF format, written by doctors who have labeled the abnormalities.

There are 5 different studies included in the dataset, each taken from different angles. These scans provide a detailed look at the spine, including the bones, discs, nerves, and surrounding tissues. They are helpful for training algorithms to classify different types of spine abnormalities.

MRI study angles in the dataset

💴 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:

Disc Degeneration: This happens when the spinal discs wear down over time.
Osteophytes: These are bony outgrowths that develop on the edges of bones.
Osteochondrosis: It’s a condition where bone and cartilage in the spine don’t grow properly.
Hemangioma: This is a noncancerous growth made of blood vessels.
Disk Extrusion: It occurs when the soft material inside a spinal disc pushes out through a crack in the tough outer layer.
Spondylitis: This is inflammation of the vertebrae in the spine.


Researchers and healthcare experts can use this dataset to learn more about spine-related issues like slipped discs, narrow spinal canals, abnormal spine curvature (scoliosis), and fractures. They can also use the dataset to create and test new ways of taking images, computer programs for analyzing images, and AI systems for diagnosing spine problems automatically.

Spinal Cord Images – Spine MRI Dataset
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Spine MRI Dataset -Anomaly Detection
Spinal Vertebrae Segmentation Dataset

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/data-market to discuss your requirements, learn about the price and buy the dataset


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_5.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, labeling, positive, negative, symptoms, clinical, sensory, varicella, syndrome, predictors, diagnosis, RSNA cervical, image training, segmentation meta, spine training, 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 in medical imaging, spinal abnormalities, image classification, neural network spine scans, MRI data analysis, deep learning in 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.

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