Automated analysis of paraspinal muscles: segmentation and multi-parameter quantification in lumbar CT using convolutional neural network.
Authors
Affiliations (9)
Affiliations (9)
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
- Department of Orthopedics, First People's Hospital of Yunnan Province, Kunming, China.
- Intelligent Orthopedics Medical Technology Research Centre, Kunming University of Science and Technology, Kunming, China.
- School of Software Engineering, Sun Yat-sen University, Guangzhou, China.
- Department of Gastrointestinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. [email protected].
- Faculty of Health Sciences, University of Macau, Macao, China. [email protected].
- School of Software Engineering, Sun Yat-sen University, Guangzhou, China. [email protected].
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. [email protected].
Abstract
Manual segmentation in lumbar paraspinal muscle studies is time-consuming, subject to variability, and limits comprehensive evaluation. To solve the problems, we aim to develop a deep learning algorithm for the automatic segmentation and multi-parameter quantification of eight paraspinal muscles in lumbar CT. We collected CT images covering the range from the superior endplate of the L1 vertebra to the inferior endplate of the S1 vertebra. The dataset was partitioned into training, validation, and test sets in a ratio of 7:1:2. Six convolutional neural networks were employed to automatically segment the bilateral psoas major, quadratus lumborum, erector spinae, and multifidus across all slices. Model performance was evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and mean Intersection over Union (mIoU). Muscle parameters including cross-sectional area, muscle volume, fat infiltration, CT density, and paraspinal muscle index were calculated. The Intraclass Correlation Coefficient [ICC(3,1)] was used to assess agreement between model's segmentations and manual ground truth. 100 lumbar CT scans (age, 55.02 ± 16.2; 62 female) were used for model development. The mean DSC across all six models ranged from 0.754 to 0.903. TransUNet demonstrated the best overall (DSC, 0.903; 95% CI: 0.902, 0.913) (HD, 9.934 mm; 95% CI: 9.815, 11.902) (mIoU, 0.841; 95% CI: 0.839, 0.850). For individual muscles segmented by TransUNet, the highest DSC was 0.936 (95% CI: 0.934-0.938) for the right multifidus. The mean ICC for all parameters was 0.931. The highest ICC value was 0.999 (p < 0.001). The deep learning tool enables automatic and accurate segmentation of eight lumbar paraspinal muscles. Muscle parameter measurements derived from model's segmentations and manual ground truth exhibit high agreement. Accurate segmentation and quantification of paraspinal muscle parameters facilitates large-scale epidemiological studies of paraspinal muscles and spine-related diseases, overcoming the limitations in manual methods.