Convolutional neural networks for the automatic segmentation of lumbar paraspinal muscles in people with low back pain.

ABSTRACT: The size, shape, and composition of paraspinal muscles have been widely reported in disorders of the cervical and lumbar spine. Measures of size, shape, and composition have required time-consuming and rater-dependent manual segmentation techniques. Convolutional neural networks (CNNs) provide alternate timesaving, state-of-the-art performance measures, which could realise clinical translation. Here we trained a CNN for the automatic segmentation of lumbar paraspinal muscles and determined the impact of CNN architecture and training choices on segmentation performance. T₂-weighted MRI axial images from 76 participants (46 female; age (SD): 45.6 (12.8) years) with low back pain were used to train CNN models to segment the multifidus, erector spinae, and psoas major muscles (left

SUBMITTER: Wesselink EO 

PROVIDER: S-EPMC9355981 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature