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

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC10930005 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature