Fully automatic classification of lumbar disc degeneration based on deep learning

Abstract

Objective To investigate the feasibility of a deep learning model for the fully automatic classification of disc degeneration based on lumbar structures on sagittal T2WI images. Methods The lumbar T2WI image data of 94 patients who underwent lumbar spine MRI examination in the Third Affiliated Hospital of Anhui Medical University from August 2020 to June 2022 were retrospectively selected，and 466 discs were obtained. The lumbar intervertebral disc were manually annotated by 2 radiologists on sagittal T2WI images．The data were randomly divided into train set (n=300)，validation set (n=72)，and test set (n=94). Firstly, a U-Net network was used to train the disc segmentation model. The evaluation indexes of the model included Dice coefficient and IoU score. Then, SpineNet network was used to train the classification model, and the evaluation indexes of the model included accuracy, sensitivity, specificity, F1 score, and ROC curves. Results In the test set, the dice coefficient and IoU values of U-Net model for lumbar disc segmentation were 0.920 and 0.853, respectively. The accuracy, specificity and sensitivity value of SpineNet classification models for lumbar disc degeneration were 0.913, 0.912 and 0.916, respectively. The ROC curve analysis showed that the AUC values for distinguishing mild to moderate, mild to serious, and moderate to serious lumbar disc degeneration were 0.89, 0.95, and 0.90, respectively. Conclusion It is feasible to realize the fully automatic classification of disc degeneration based on deep learning network.

Keywords

• lumbar
• disc degeneration
• deep learning network
• t2wi sagittal image
• u-net model
• segmentation model
• classification model