Spine Explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images

Abstract

Background context: Although quantitative measurements improve the assessment of disc degeneration, acquirement of quantitative measurements relies on manual segmentation on lumbar magnetic resonance images (MRIs), which may introduce subjective bias. To date, only a few semiautomatic systems have been developed to quantify important components on MRIs.

Purpose: To develop a deep learning based program (Spine Explorer) for automated segmentation and quantification of the vertebrae and intervertebral discs on lumbar spine MRIs.

Study design: Cross-sectional study.

Patient sample: The study was extended on the Hangzhou Lumbar Spine Study, a population-based study of mainland Chinese with focuses on lumbar degenerative changes. From this population-based database, 50 sets lumbar MRIs were randomly selected as training dataset, and another 50 as test dataset.

Outcome measures: Regions of vertebrae and discs were manually segmented on T2W sagittal MRIs to train a convolutional neural network for automated segmentation. Intersection-over-union was calculated to evaluate segmentation performance. Computational definitions were proposed to acquire quantitative morphometric and signal measurements for lumbar vertebrae and discs. MRIs in the test dataset were automatically measured with Spine Explorer and manually with ImageJ.

Methods: Intraclass correlation coefficient (ICC) were calculated to examine inter-software agreements. Correlations between disc measurements and Pfirrmann score as well as age were examined to assess measurement validity.

Results: The trained Spine Explorer automatically segments and measures a lumbar MRI in half a second, with mean Intersection-over-union of 94.7% and 92.6% for the vertebra and disc, respectively. For both vertebra and disc measurements acquired with Spine Explorer and ImageJ, the agreements were excellent (ICC=0.81~1.00). Disc measurements significantly correlated to Pfirrmann score, and greater age was associated with greater anterior disc bulging area (r=0.35~0.44) and fewer signal measurements (r=-0.62~-0.77) as automatically acquired with Spine Explorer.

Conclusions: Spine Explorer is an efficient, accurate, and reliable tool to acquire comprehensive quantitative measurements for lumbar vertebra and disc. Implication of such deep learning based program can facilitate clinical studies of the lumbar spine.

Keywords: Deep learning; Disc degeneration; II; Image processing; Level of Evidence; Lumbar spine; MRI.