Lumbar Spine MRI Dataset
Lumbar Spine MRI Dataset (Sudirman et al.)
Clinical lumbar spine MRI of adults with back pain, assembled to develop automated detection of spinal stenosis. 515 adults from one hospital in Jordan with sagittal and axial series; companion releases add segmentation labels and radiologist notes.
Overview
The Lumbar Spine MRI Dataset holds routine clinical lumbar spine MRI studies of 515 adults who reported back pain at Irbid Specialty Hospital in Jordan. It was assembled by Liverpool John Moores University and Universitas Multimedia Nusantara together with radiologists from the hospital, to develop automated detection of lumbar spinal stenosis. It is published on Mendeley Data under CC BY 4.0 and downloads without an account.
Composition
The archive contains 48,345 two-dimensional DICOM slices in 558 studies of 515 patients; some patients were scanned more than once. Every patient has T1-weighted and T2-weighted series in both sagittal and axial orientation. The axial slices mostly cover the lowest three intervertebral discs, with four to five slices per disc, while the sagittal series reach at least the last seven vertebrae and the upper sacrum. A minority of studies carry further series, such as proton-density, inversion-recovery, diffusion or post-contrast sequences. In the DICOM headers, 198 patients are female and 317 male, aged 17 to 89 years (mean 41.8) at their first study.
Acquisition
Data were collected between September 2015 and July 2016 on a single Siemens MAGNETOM ESSENZA 1.5 T scanner, with turbo spin echo sequences. Most slices are 320 x 320 pixels at 12 bits. Axial slices are 4 mm thick with 4.4 mm spacing and 0.6875 mm in-plane pixel size. The authors removed 60 of 575 collected studies that lacked the required series, were of poor quality, showed fused or destroyed vertebrae, or came from patients younger than 17.
Annotations
The image archive itself carries no labels. Two companion releases on Mendeley Data, also under CC BY 4.0, provide them: radiologist notes on findings such as disc bulges, stenosis, Modic changes and spondylolisthesis for every patient, and ground truth label images for 1,545 axial T1-weighted slices of the last three discs. Five labellers supervised by a radiologist marked the disc, posterior element, thecal sac and the area between the anterior and posterior vertebral elements.
Known limitations
All data come from one hospital and one scanner model, which limits variation in scanner and protocol. Studies were filtered for quality and for usable T1-T2 registration, so harder clinical cases are under-represented. Files use Siemens .ima naming and keep series names, so sequences have to be identified from folder names or headers.
Cohort
Aggregate numbers from the sources below. Bars are relative to the 515 subjects.
Sex
- Female 198 38%
- Male 317 62%
DICOM PatientSex in MRI_Data.zip (CC BY 4.0), counted from the archive's file list and the DICOM header of the first non-localizer image of each patient's first study
Contrast / sequence
Groups can overlap
- T1-weighted 515 100%
- T2-weighted 515 100%
Section II.A, p. 43490 in Al-Kafri et al. 2019, IEEE Access
Field strength
- 1.5 T 515 100%
DICOM MagneticFieldStrength in MRI_Data.zip (CC BY 4.0), counted from the archive's file list and the DICOM header of the first non-localizer image of each patient's first study
Age
mean 41.8 ± 13.4, median 40, range 17 to 89
DICOM PatientAge in MRI_Data.zip (CC BY 4.0), counted from the archive's file list and the DICOM header of the first non-localizer image of each patient's first study
Scanner vendor
- Siemens Healthineers 515 100%
DICOM Manufacturer in MRI_Data.zip (CC BY 4.0), counted from the archive's file list and the DICOM header of the first non-localizer image of each patient's first study
License and access
Our reading of the license, not legal advice. Before you use the data, read the original license and confirm that your use is allowed. We take no responsibility for how you use a dataset. Full disclaimer
Download without an account
Creative Commons Attribution 4.0 International
Use, share and adapt the data for any purpose, including commercial use, as long as you credit the creators.
What you can do
- Yes
- Yes
- Yes
- Yes
- Yes
What you can share
- Yes
- Yes
- Yes
What you must do
- Yes
- Share alike No
- No
- No
- Manuscript review No
- Release code No
- Return results No
- Delete after use No
Limits
- No
- Location limits No
Citation
Sudirman S, Al Kafri A, Natalia F, Meidia H, Afriliana N, Al-Rashdan W, Bashtawi M, Al-Jumaily M. Lumbar Spine MRI Dataset. Mendeley Data, V2 (2019). doi:10.17632/k57fr854j2.2. Al-Kafri AS, et al. Boundary Delineation of MRI Images for Lumbar Spinal Stenosis Detection Through Semantic Segmentation Using Deep Neural Networks. IEEE Access 7, 43487-43501 (2019).
Sources
Every number on this page comes from one of these documents. Each chart names the table or page it is taken from. The raw numbers are in stats.csv.