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Lumbar Spine MRI Scans - 2,4 million studies

The dataset contains MRI images of the lumbar vertebras and lumbar discs, focusing on the vertebras and lumbar intervertebral discs. The scans are accompanied with medical reports to help in diagnosing spine diseases such as degenerative spine conditions, lumbar degenerative disorders, and disc herniations. This dataset emphasizes spine magnetic resonance imaging of the lumbar region and spinal canal and capture detailed spine imaging using sagittal T2-weighted images - Get the data

The dataset supports segmentation algorithms and classification models, aiming for accurate automatic segmentations and classification results. Deep learning techniques can be applied to medical images to assess spinal stenosis, detect degenerative changes, and segment spinal structures. The spinal pathology covered includes conditions like spinal cord compression, canal stenosis, and other lumbar spinal disorders. The dataset also includes sagittal views and axial views, making it suitable for machine learning and medical diagnosis tasks.

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Content

The dataset includes:

  • ST000001: includes subfolders with 9 studies. Each study includes MRI-scans in .dcm and .jpg formats,
  • DICOMDIR: includes information about the patient's condition and links to access files,
  • Lumbar_Spine_MRI.pdf: includes medical report, provided by the radiologist,
  • .csv file: includes the number of studies by conditions and methods of study

Medical reports include the following data:

  • Type of a study,
  • MRI machine (mostly Philips Intera 1.5T),
  • Patient's demographic information (age, sex, race),
  • Brief anamnesis of the disease (complaints),
  • Description of the case,
  • Preliminary diagnosis,
  • Recommendations on the further actions

All patients consented to the publication of data, data is unidentified

This multi-center lumbar dataset is invaluable for exploring disc disease, back pains, and degenerative changes, offering rich diagnostic imaging data and performance values for classification models and segmentation challenges in spinal pathology analysis.

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