Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network

Giam Minh Trinh, Hao Chiang Shao, Kevin Li Chun Hsieh, Ching Yu Lee, Hsiao Wei Liu, Chen Wei Lai, Sen Yi Chou, Pei I. Tsai, Kuan Jen Chen, Fang Chieh Chang, Meng Huang Wu, Tsung Jen Huang

研究成果: 雜誌貢獻文章同行評審


Spondylolisthesis refers to the displacement of a vertebral body relative to the vertrabra below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the lower lumbar spine, especially in women over the age of 60. The prevalence of spondylolisthesis is expected to rise as the global population ages, requiring prudent action to promptly identify it in clinical settings. The goal of this study was to develop a computer-aided diagnostic (CADx) algorithm, LumbarNet, and to evaluate the efficiency of this model in automatically detecting spondylolisthesis from lumbar X-ray images. Built upon U-Net, feature fusion module (FFM) and collaborating with (i) a P-grade, (ii) a piecewise slope detection (PSD) scheme, and (iii) a dynamic shift (DS), LumbarNet was able to analyze complex structural patterns on lumbar X-ray images, including true lateral, flexion, and extension lateral views. Our results showed that the model achieved a mean intersection over union (mIOU) value of 0.88 in vertebral region segmentation and an accuracy of 88.83% in vertebral slip detection. We conclude that LumbarNet outperformed U-Net, a commonly used method in medical image segmentation, and could serve as a reliable method to identify spondylolisthesis.

期刊Journal of Clinical Medicine
出版狀態已發佈 - 9月 2022

ASJC Scopus subject areas

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