Department of Biomedical Research Institute, Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon, Republic of Korea.
Department of Orthopaedic Surgery, School of Medicine, Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon, Republic of Korea.
BMC Musculoskelet Disord. 2024 Sep 6;25(1):716. doi: 10.1186/s12891-024-07777-4.
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.
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.
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.
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.
准确分割脊柱肌肉在分析肌肉骨骼疾病和设计有效的康复策略方面起着至关重要的作用。各种成像技术，如 MRI，已被用于获取肌肉图像，但由于肌肉结构的固有复杂性和可变性，分割过程仍然复杂且具有挑战性。在这项系统评价中，我们调查和评估了自动分割脊柱肌肉的方法。
本研究的数据来自 PubMed/MEDLINE 数据库，采用的搜索方法包括在标题、摘要和关键词中使用“Spine muscle segmentation”一词，以确保全面系统地收集相关研究。本研究不包括系统评价。
在 369 项相关研究中，我们重点关注了 12 项具体研究。所有研究都集中在脊柱肌肉的分割上，都使用了 MRI，在这项系统评价中，研究对象包括健康志愿者、背痛患者、ASD 患者等。MRI 成像设备来自多个制造商，包括西门子、GE。研究包括使用 AI 进行自动分割、使用 PDFF 进行分割以及使用 ROI 进行分割。
尽管脊柱肌肉分割技术取得了进展，但仍存在挑战。需要提高分割算法的准确性和精度，以准确描绘脊柱中的不同肌肉结构。稳健性对于图像质量、伪影和患者特异性特征的变化至关重要，以获得可靠的分割结果。此外，为了开发和评估新的分割算法，需要有用于训练和验证目的的带注释数据集。未来的研究应集中解决这些挑战，并开发更强大、更准确的脊柱肌肉分割技术，以增强肌肉骨骼疾病的临床评估和治疗计划。
