Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

LumbarNet: A Deep Learning Network for the Automated Detection of Lumbar Spondylolisthesis From X-Ray Images

Version 1 : Received: 2 June 2022 / Approved: 3 June 2022 / Online: 3 June 2022 (10:15:57 CEST)

How to cite: Trinh, G.M.; Shao, H.; Hsieh, K.L.; Lee, C.; Liu, H.; Lai, C.; Chou, S.; Tsai, P.; Chen, K.; Chang, F.; Wu, M.; Huang, T. LumbarNet: A Deep Learning Network for the Automated Detection of Lumbar Spondylolisthesis From X-Ray Images. Preprints 2022, 2022060043 (doi: 10.20944/preprints202206.0043.v1). Trinh, G.M.; Shao, H.; Hsieh, K.L.; Lee, C.; Liu, H.; Lai, C.; Chou, S.; Tsai, P.; Chen, K.; Chang, F.; Wu, M.; Huang, T. LumbarNet: A Deep Learning Network for the Automated Detection of Lumbar Spondylolisthesis From X-Ray Images. Preprints 2022, 2022060043 (doi: 10.20944/preprints202206.0043.v1).

Abstract

A common spinal condition, spondylolisthesis is the presence of a relative back or forth displacement between the upper and lower vertebra due to one vertebra being oriented away from the smooth curvature of a normal spine. Aging-related illnesses such as degenerative spondylolisthesis are especially burdensome on social welfare and health-care systems in an aging society, especially radiologists and clinical physicians. Therefore, we proposed a computer aided diagnosis algorithm, named LumbarNet, for vertebral slippage detection on clinical X-ray images. Collaborating with i) a P-grade, ii) a piecewise slope detection scheme, and iii) a dynamic shift detection routine, LumbarNet was thus specialized for analyzing complex structural patterns in lumbar spine X-ray images and outcompeted other U-Net based methods. Extensive experiments on lumbar spine X-ray images in standard clinical practices showed that LumbarNet achieved a mean intersection over union value of 0.88 in vertebral region detection and an accuracy of 88.83% in vertebral slippage detection.

Keywords

deep learning; lumbarnet; lumbar spine; spondylolisthesis; u-net

Subject

MEDICINE & PHARMACOLOGY, Sport Sciences & Therapy

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