Deep Learning-Based Segmentation of Cervical Posterior Longitudinal Ligament Ossification in Computed Tomography Images and Assessment of Spinal Cord Compression: A Two-Center Study.

Abstract

Objective: This study aims to develop a fully automated, computed tomography (CT)-based deep learning (DL) model to segment ossified lesions of the posterior longitudinal ligament and to measure the thickness of the ossified material and calculate the cervical spinal cord compression factor.

Methods: A total of 307 patients were enrolled, with 260 patients from Shanghai Changzheng Hospital, and 47 patients from the Traditional Chinese Medicine Hospital of Southwest Medical University. CT images were used to manually segment the Ossification of the posterior longitudinal ligament (OPLL) by 4 experienced radiologists. The DL model employing a 3-dimensional U-Net framework was developed to segment the OPLLs. The system also measures the thickness of the ossified material at its thickest point and the diameter of the spinal canal at the corresponding level. Segmentation performance was evaluated using the Dice Similarity Coefficient, Average Surface Distance, and intraclass correlation coefficient (ICC) between ground truth and segmentation volumes. Concordance between the radiologists' and the DL system's measurements of the ossified material thickness, residual spinal canal diameter at maximum compression, and cervical spinal cord compression coefficient was assessed in a randomly selected subset of 30 cases from the training set using ICCs and Bland-Altman plots.

Results: The DL system demonstrated average Dice Similarity Coefficient of 0.81, 0.75, and 0.71 for the training, internal validation, and external test sets, respectively. The mean Average Surface Distance was 1.30 for the training set, 2.35 for the internal validation set, and 2.63 for the external test set. The ICC values of 0.958 for the thickness of the ossified material and 0.974 for the residual canal diameter measurement.

Conclusions: The proposed DL model effectively detects and separates ossification foci in OPLL on CT images. It exhibits comparable performance to radiologists in quantifying spinal cord compression metrics.