A quantitative evaluation of the deep learning model of segmentation and measurement of cervical spine MRI in healthy adults.

ABSTRACT

PURPOSE: To evaluate the 3D U-Net model for automatic segmentation and measurement of cervical spine structures using magnetic resonance (MR) images of healthy adults. MATERIALS AND METHODS: MR images of the cervical spine from 160 healthy adults were collected retrospectively. A previously constructed deep-learning model was used to automatically segment anatomical structures. Segmentation and localization results were checked by experienced radiologists. Pearson's correlation analyses were conducted to examine relationships between patient and image parameters. RESULTS: No measurement was significantly correlated with age or sex. The mean values of the areas of the subarachnoid space and spinal cord from the C2/3 (cervical spine 2-3) to C6/7 intervertebral disc levels were 102.85-358.12 mm2 and 53.71-110.32 mm2 , respectively. The ratios of the areas of the spinal cord to the subarachnoid space were 0.25-0.68. The transverse and anterior-posterior diameters of the subarachnoid space were 14.77-26.56 mm and 7.38-17.58 mm, respectively. The transverse and anterior-posterior diameters of the spinal cord were 9.11-16.02 mm and 5.47-10.12 mm, respectively. CONCLUSION: A deep learning model based on 3D U-Net automatically segmented and performed measurements on cervical spine MR images from healthy adults, paving the way for quantitative diagnosis models for spinal cord diseases.