How-to

Upload a zip file containing DICOM files below. The scan will then be processed, typically within 1 minute. For each scan, this demo produces radiological gradings described in the main page. When a scan is processed, this page will automatically be redirected to results page. You can also enter in your e-mail address where we will send you an e-mail containing a link to the results.

The ZIP file should:

• be less than 20MB.
• not contain any folders.
• only contain raw DICOM files of a lumbar T2-weighted sagittal scan.
• only contain scan of one individual from a single session (one DICOM series). Scans taken at another time (e.g. in a longitudinal/prospective study) should be submitted as a separate ZIP.
• Detailed instructions of making the ZIP file from OsiriX can be found here.

Disclaimer

• This is not a diagnostics tool nor a medical device and should only be used for research.
• We do not store the data after processing. Please keep the name of the ZIP file you submit and the corresponding reference number.

Details

• We process all the slices in a DICOM volume but for ease of use we only show the midsagittal slice in the results.
• If you use the data produced from this website, please cite [1] or [2], preferably both.
• Version 0.1

Examples

• Sample 1: Zip, Result Case courtesy of A.Prof Frank Gaillard, Radiopaedia.org. From the case rID: 35543
• Sample 2: Zip, Result Case courtesy of Dr Henry Knipe, Radiopaedia.org. From the case rID: 56636

More Information

More details about this work is available at http://www.robots.ox.ac.uk/~vgg/research/spine/

For any queries or to add your own grading system, contact amirj@robots.ox.ac.uk

References

[1] A. Jamaludin, M. Lootus, T. Kadir, A. Zisserman, J. Urban, M. C. Battié, J. Fairbank, and I. McCall. Issls prize in bioengineering science 2017: Automation of reading of radiological features from magnetic resonance images (mris) of the lumbar spine without human intervention is comparable with an expert radiologist. European Spine Journal, 26(5):1374–1383, May 2017.

[2] A. Jamaludin, T. Kadir, and A. Zisserman. SpineNet: Automated classification and evidence visualization in spinal MRIs. Medical Image Analysis, 41 (Supplement C):63 – 73, 2017.