Spine muscle auto segmentation techniques in MRI imaging: a systematic review

• Published in: BMC musculoskeletal disorders Vol. 25; no. 1; pp. 716 - 8
• Main Authors: Kim, Hyun-Bin, Kim, Hyeon-Su, Kim, Shin-June, Yoo, Jun-Il
• Format: Journal Article
• Language: English
• Published: London BioMed Central 06.09.2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accuracy; Algorithms; Artificial intelligence; Back; Back pain; Backache; Datasets; Deep learning; Design; Diagnosis; Epidemiology; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Internal Medicine; Machine learning; Magnetic resonance imaging; Magnetic resonance imaging (MRI); Magnetic Resonance Imaging - methods; Medical colleges; Medical imaging equipment; Medical research; Medicine; Medicine & Public Health; Medicine, Experimental; Methods; Muscles; Musculoskeletal diseases; Neural networks; Orthopedics; Paraspinal Muscles - diagnostic imaging; Pathology; Patients; Rehabilitation; Rheumatology; Segmentation; Spine - diagnostic imaging; Spine muscles; Sports Medicine; Systematic Review; South Korea; Deep learning; Magnetic resonance imaging (MRI); Spine muscles; Segmentation; Machine learning
• ISSN: 1471-2474; 1471-2474
• DOI: 10.1186/s12891-024-07777-4

Background The accurate segmentation of spine muscles plays a crucial role in analyzing musculoskeletal disorders and designing effective rehabilitation strategies. Various imaging techniques such as MRI have been utilized to acquire muscle images, but the segmentation process remains complex and challenging due to the inherent complexity and variability of muscle structures. In this systematic review, we investigate and evaluate methods for automatic segmentation of spinal muscles. Methods Data for this study were obtained from PubMed/MEDLINE databases, employing a search methodology that includes the terms 'Segmentation spine muscle’ within the title, abstract, and keywords to ensure a comprehensive and systematic compilation of relevant studies. Systematic reviews were not included in the study. Results Out of 369 related studies, we focused on 12 specific studies. All studies focused on segmentation of spine muscle use MRI, in this systematic review subjects such as healthy volunteers, back pain patients, ASD patient were included. MRI imaging was performed on devices from several manufacturers, including Siemens, GE. The study included automatic segmentation using AI, segmentation using PDFF, and segmentation using ROI. Conclusion Despite advancements in spine muscle segmentation techniques, challenges still exist. The accuracy and precision of segmentation algorithms need to be improved to accurately delineate the different muscle structures in the spine. Robustness to variations in image quality, artifacts, and patient-specific characteristics is crucial for reliable segmentation results. Additionally, the availability of annotated datasets for training and validation purposes is essential for the development and evaluation of new segmentation algorithms. Future research should focus on addressing these challenges and developing more robust and accurate spine muscle segmentation techniques to enhance clinical assessment and treatment planning for musculoskeletal disorders.