An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

Abstract

Purpose. Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset. Methods. A set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Psoas major, quadratus lumborum, erector spinae, and multifidus were manually segmented in all available slices. The dataset was used to train and validate a deep neural network able to segment muscles automatically. Subsequently, the network was externally validated on images purposely acquired from 22 healthy volunteers. Results. The Jaccard index for the individual muscles calculated for the 22 subjects of the external validation set ranged between 0.862 and 0.935, demonstrating a generally excellent performance of the network. Cross-sectional area and fat fraction of the muscles were in agreement with published data. Conclusions. The externally validated deep neural network was able to perform the segmentation of the paravertebral muscles in axial MRI scans in an accurate and fully automated manner, and is therefore a suitable tool to perform large-scale studies in the field of spinal disorders and sarcopenia, overcoming the limitations of non-automated methods.

Correlation study between the fatty infiltration rate and some lumbar diseases

“…In clinical practice, LBP refers to the lower back, lumbosacral, hip pain as the main manifestation of a syndrome.Surveys show that 80% of adults will have LBP at different times in their life, with an incidence of 7.6% to 37%. For people under 45 years old, non-speci c LBP is the main cause of limited activity [29] .Previous studies [30][31][32][33][34] have indicated that there is a close relationship between the paravertebral muscle and non-speci c LBP.Since paravertebral muscles play a leading role in the stability of the spine, paravertebral muscle atrophy and muscle ber characteristics changes caused by the fear of movement and protective posture of patients with chronic LBP are often regarded as the main reasons for recurrent episodes of chronic LBP.Our study showed that the total FIR in LBP group and LDH group was higher than that in normal group. The higher the VAS score and ODI score in the LBP group and the LDH group, the higher the FIR.This also con rmed that patients in the LBP group and the LDH group were related to the FIR in the parathyteal muscle, and the greater the FIR, the greater the lumbago.…”

Advancing spine care through AI and machine learning: overview and applications

“…Automatic tissue segmentation from MRI data showcases the ability of machine learning to perform complex tasks, such as vertebrae recognition and segmentation with a high degree of accuracy, comparable to manual segmentation by physicians (56,57,58). Attention has also been devoted to the segmentation of paravertebral muscles (59,60,61), the properties of which (such as cross-sectional areas and fatty infiltration) are known to be associated with disability and treatment outcomes (62).…”