Feasibility Study of artificial neural network model automating segmentation and measuring disc and deep extensor muscles on axial cervical magnetic resonance images

English Keywords:Cervical spine  Deep extensor muscles  MRI  Deep learning  Intelligent segmentation  Neural network model

English Abstract:

  【Abstract】 Objectives: To develop and establish a new algorithm for the automated segmentation and measurement of deep extensor muscles on axial cervical magnetic resonance images based on deep learning. Methods: 78 adult healthy volunteers(30 males and 48 females with an average age of 45.4±12.6 years) were recruited and cervical spine magnetic resonance images were acquired using a 3.0T scanner in our hospital. A total of 345 axial T2WI MR cervical spine images through the middle of the C2/3-C6/7 disc were obtained. 276 MR images (80%) were used to train and validate a deep learning model which was developed based on Mask region-based convolutional neural network (Mask R-CNN) to segment and measure the deep extensor cross-sectional area (DCSA), functional cross-sectional area (FCSA), and intervertebral disc cross-sectional area (IDCSA) at the same level automatically. 69 images (20%) were classified as test set, the DCSA, FCSA and IDCSA of test set were manually measured by 2 surgeons for comparison with the results of model measurements. The mean intersection over union(MIoU) and mean pixel accuracy (MPA) were used to evaluate the segmentation effect of the model. Bland-Altman methods and intraclass correlation coefficients (ICCs) were used to examine the agreement between artificial measurement groups and model measurement. Results: The segmentation algorithm of this deep learning model achieved an overall MPA of 0.920 and an overall MIoU of 0.912. The MPA of IDCSA, left DCSA and right DCSA were 0.946, 0.917 and 0.911, respectively, and the MIoU were 0.934, 0.908 and 0.899, respectively. In the test set, the results of algorithm measurements of IDCSA, left and right DCSA, left and right FCSA (3.28±1.02cm2, 2.84±1.11cm2, 2.86±1.09cm2, 2.19±0.89cm2, and 2.23±0.86cm2, respectively) were similar to the results of artificial group (3.35±0.99cm2, 3.19±1.16cm2, 3.16±1.12cm2, 2.49±0.99cm2, and 2.42±0.88cm2, respectively). Interclass correlation coefficients (ICCs) were excellent for artificial measurement groups and the deep learning algorithm measurement group (0.852-0.914), and Bland-Altman plots also showed high levels of agreement. The mean artificial measurement time was 256.5±53.3s, and the mean model measurement time was 0.109±0.402s, which were statistically different(P<0.001). Conclusions: This algorithm automatically segments and measures cervical deep extensor muscles on axial MRI with comparable accuracy to spine surgeons.