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Kim HB, Kim HS, Kim SJ, Yoo JI. Spine muscle auto segmentation techniques in MRI imaging: a systematic review. BMC Musculoskelet Disord 2024; 25:716. [PMID: 39243080 PMCID: PMC11378543 DOI: 10.1186/s12891-024-07777-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 08/14/2024] [Indexed: 09/09/2024] Open
Abstract
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.
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Affiliation(s)
- Hyun-Bin Kim
- Department of Biomedical Research Institute, Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon, Republic of Korea
| | - Hyeon-Su Kim
- Department of Biomedical Research Institute, Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon, Republic of Korea
| | - Shin-June Kim
- Department of Biomedical Research Institute, Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon, Republic of Korea
| | - Jun-Il Yoo
- Department of Orthopaedic Surgery, School of Medicine, Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon, Republic of Korea.
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Wang X, Xu M. Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis. PLoS One 2023; 18:e0285015. [PMID: 37363923 DOI: 10.1371/journal.pone.0285015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/13/2023] [Indexed: 06/28/2023] Open
Abstract
OBJECTIVE This work was aimed to analyze the effect of vitamin energy drink on muscle fatigue by surface electromyography (SEMG) and ultrasonic bioimaging (USBI). METHODS 20 healthy men were selected to do increasing load fatigue test. Surface electromyographic signals and ultrasonic biological images were collected based on wavelet threshold function with improved thresholds. Time domain and frequency domain characteristic integrated electromyography (IEMG), root mean square amplitude (RMS), average power frequency (MPF), and surface and deep muscle morphological changes were analyzed. Hemoglobin concentration (HB), red blood cell number (RBC), mean volume of red blood cell (MCV), blood lactic acid (BLA), malondialdehyde (MDA), and phosphocreatine kinase (CK) were measured. RESULTS 1) the Accuracy (94.10%), Sensitivity (94.43%), Specificity (93.75%), and Precision (94.07%) of the long and short-term memory (LSTM) specificity for muscle fatigue recognition were higher than those of other models. 2) Compared with the control group, the levels of BLA, MDA, and CK in the experimental group were decreased and HB levels were increased after exercise (P < 0.05). 3) IEMG and RMS of the experimental group were higher than those of the control group, and increased with time (P < 0.05). 4) The mean amplitude of the response signal decreased with time. Compared with the control group, the surface muscle thickness, deep muscle thickness, total muscle thickness, contrast, and homogeneity (HOM) decreased in the experimental group; while the angular second moment (ASM) and contrast increased, showing great differences (P < 0.05). CONCLUSION Surface electromyographic signal and ultrasonic biological image can be used as auxiliary monitoring techniques for muscle fatigue during exercise. Drinking vitamin energy drinks before exercise can relieve physical fatigue to a certain extent and promote the maintenance of muscle microstructure.
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Affiliation(s)
- Xindi Wang
- School of Aerospace, Harbin Institute of Technology, Harbin, Heilongjiang, China
- China Basketball College, Beijing Sport University, Beijing, Beijing, China
| | - Mengtao Xu
- China Basketball College, Beijing Sport University, Beijing, Beijing, China
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Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches. Invest Radiol 2023; 58:3-13. [PMID: 36070548 DOI: 10.1097/rli.0000000000000907] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
ABSTRACT Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging- and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics- and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.
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Liu JJ, Wang YZ, Chen N, Wang QN, Liu L, Li Y, Lei L, Wu Y. Hypothesis generation: Quantitative research to levator ani muscle injury based on MRI texture analysis. J Obstet Gynaecol Res 2022; 48:3269-3278. [PMID: 36167929 DOI: 10.1111/jog.15440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 11/30/2022]
Abstract
AIM Patients with pelvic organ prolapse (POP) mostly have injury to the levator ani muscle (LAM). We aimed to assess LAM injury in POP patients by quantifying texture feature (TF) ratios between the LAM and the obturator internus muscle (OIM) using texture analysis. METHODS This study retrospectively enrolled 32 participants, including 24 patients with POP and eight people with normal pelvic floor muscles. TFs of the LAM and the OIM were extracted using LIFEx version 6.30, and an independent samples t-test was performed to determine TF ratios characterizing LAM injury. After dimension reduction and binary logic analysis, the optimal TF ratio was obtained and the LAM injury quantitative evaluation was proposed. Spearman's correlation was performed to explore the correlations between TF ratios and clinical characteristics. We compared the diagnostic performance of quantitative evaluation and visual evaluation. RESULTS There were significant differences in 13 TF ratios between the POP and control groups. The area under the receiver operating characteristic curve of the integrated TF ratio was 0.948. Integrated TF ratio was significantly correlated with body mass index, pregnancies, and vaginal deliveries but had no correlation with LAM volume, hiatal area or abortions. Compared with the visual evaluation, the diagnostic accuracy of the quantitative evaluation had improved by 63.2% and 14.3% in the "minor defect" and "major defect" categories, respectively. CONCLUSION The integrated TF ratio can be used as a new quantifiable index to characterize LAM injury. The TF evaluation provides a potential role in LAM injury noninvasive diagnostic.
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Affiliation(s)
- Jing Jing Liu
- Faculty of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
| | - Yan Zhou Wang
- Department of Gynecology and Obstetrics, First Affiliated Hospital of Army Medical University, Chongqing, China
| | - Na Chen
- Faculty of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
| | - Qian Nan Wang
- Faculty of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
| | - Li Liu
- Faculty of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
| | - Ying Li
- Faculty of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
| | - Ling Lei
- Department of Gynecology and Obstetrics, First Affiliated Hospital of Army Medical University, Chongqing, China.,Department of Gynecology, The People Hospital of Anshun, Anshun City, China
| | - Yi Wu
- Faculty of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
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Leonhardt Y, Dieckmeyer M, Zoffl F, Feuerriegel GC, Sollmann N, Junker D, Greve T, Holzapfel C, Hauner H, Subburaj K, Kirschke JS, Karampinos DC, Zimmer C, Makowski MR, Baum T, Burian E. Associations of Texture Features of Proton Density Fat Fraction Maps between Lumbar Vertebral Bone Marrow and Paraspinal Musculature. Biomedicines 2022; 10:biomedicines10092075. [PMID: 36140176 PMCID: PMC9495779 DOI: 10.3390/biomedicines10092075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 08/21/2022] [Accepted: 08/22/2022] [Indexed: 12/02/2022] Open
Abstract
Chemical shift encoding-based water−fat MRI (CSE-MRI)-derived proton density fat fraction (PDFF) has been used for non-invasive assessment of regional body fat distributions. More recently, texture analysis (TA) has been proposed to reveal even more detailed information about the vertebral or muscular composition beyond PDFF. The aim of this study was to investigate associations between vertebral bone marrow and paraspinal muscle texture features derived from CSE-MRI-based PDFF maps in a cohort of healthy subjects. In this study, 44 healthy subjects (13 males, 55 ± 30 years; 31 females, 39 ± 17 years) underwent 3T MRI including a six-echo three-dimensional (3D) spoiled gradient echo sequence used for CSE-MRI at the lumbar spine and the paraspinal musculature. The erector spinae muscles (ES), the psoas muscles (PS), and the vertebral bodies L1-4 (LS) were manually segmented. Mean PDFF values and texture features were extracted for each compartment. Features were compared between males and females using logistic regression analysis adjusted for age and body mass index (BMI). All texture features of ES except for Sum Average were significantly (p < 0.05) different between men and women. The three global texture features (Variance, Skewness, Kurtosis) for PS as well as LS showed a significant difference between male and female subjects (p < 0.05). Mean PDFF measured in PS and ES was significantly higher in females, but no difference was found for the vertebral bone marrow’s PDFF. Partial correlation analysis between the texture features of the spine and the paraspinal muscles revealed a highly significant correlation for Variance(global) (r = 0.61 for ES, r = 0.62 for PS; p < 0.001 respectively). Texture analysis using PDFF maps based on CSE-MRI revealed differences between healthy male and female subjects. Global texture features in the lumbar vertebral bone marrow allowed for differentiation between men and women, when the overall PDFF was not significantly different, indicating that PDFF maps may contain detailed and subtle textural information beyond fat fraction. The observed significant correlation of Variance(global) suggests a metabolic interrelationship between vertebral bone marrow and the paraspinal muscles.
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Affiliation(s)
- Yannik Leonhardt
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- Correspondence:
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Florian Zoffl
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Georg C. Feuerriegel
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, 89070 Ulm, Germany
| | - Daniela Junker
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Tobias Greve
- Department of Neurosurgery, University Hospital, Ludwig-Maximilians-University (LMU) Munich, 81377 Munich, Germany
| | - Christina Holzapfel
- Institute of Nutritional Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Hans Hauner
- Institute of Nutritional Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | | | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Dimitrios C. Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Marcus R. Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Egon Burian
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
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Editorial on Special Issue “Spine Imaging: Novel Image Acquisition Techniques and Analysis Tools”. Diagnostics (Basel) 2022; 12:diagnostics12061361. [PMID: 35741171 PMCID: PMC9221602 DOI: 10.3390/diagnostics12061361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 05/28/2022] [Indexed: 11/23/2022] Open
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Burian E, Becherucci EA, Junker D, Sollmann N, Greve T, Hauner H, Zimmer C, Kirschke JS, Karampinos DC, Subburaj K, Baum T, Dieckmeyer M. Association of Cervical and Lumbar Paraspinal Muscle Composition Using Texture Analysis of MR-Based Proton Density Fat Fraction Maps. Diagnostics (Basel) 2021; 11:diagnostics11101929. [PMID: 34679627 PMCID: PMC8534863 DOI: 10.3390/diagnostics11101929] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 11/16/2022] Open
Abstract
In this study, the associations of cervical and lumbar paraspinal musculature based on a texture analysis of proton density fat fraction (PDFF) maps were investigated to identify gender- and anatomical location-specific structural patterns. Seventy-nine volunteers (25 men, 54 women) participated in the present study (mean age ± standard deviation: men: 43.7 ± 24.6 years; women: 37.1 ± 14.0 years). Using manual segmentations of the PDFF maps, texture analysis was performed and texture features were extracted. A significant difference in the mean PDFF between men and women was observed in the erector spinae muscle (p < 0.0001), whereas the mean PDFF did not significantly differ in the cervical musculature and the psoas muscle (p > 0.05 each). Among others, Variance(global) and Kurtosis(global) showed significantly higher values in men than in women in all included muscle groups (p < 0.001). Not only the mean PDFF values (p < 0.001) but also Variance(global) (p < 0.001), Energy (p < 0.001), Entropy (p = 0.01), Homogeneity (p < 0.001), and Correlation (p = 0.037) differed significantly between the three muscle compartments. The cervical and lumbar paraspinal musculature composition seems to be gender-specific and has anatomical location-specific structural patterns.
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Affiliation(s)
- Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany; (E.A.B.); (N.S.); (C.Z.); (J.S.K.); (T.B.); (M.D.)
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany; (D.J.); (D.C.K.)
- Correspondence:
| | - Edoardo A. Becherucci
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany; (E.A.B.); (N.S.); (C.Z.); (J.S.K.); (T.B.); (M.D.)
| | - Daniela Junker
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany; (D.J.); (D.C.K.)
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany; (E.A.B.); (N.S.); (C.Z.); (J.S.K.); (T.B.); (M.D.)
- TUM-Neuroimaging Center, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, 89081 Ulm, Germany
| | - Tobias Greve
- Department of Neurosurgery, University of Munich, 81377 Munich, Germany;
| | - Hans Hauner
- Institute of Nutritional Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 80992 Munich, Germany;
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany; (E.A.B.); (N.S.); (C.Z.); (J.S.K.); (T.B.); (M.D.)
- TUM-Neuroimaging Center, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany; (E.A.B.); (N.S.); (C.Z.); (J.S.K.); (T.B.); (M.D.)
- TUM-Neuroimaging Center, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Dimitrios C. Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany; (D.J.); (D.C.K.)
| | - Karupppasamy Subburaj
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore;
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany; (E.A.B.); (N.S.); (C.Z.); (J.S.K.); (T.B.); (M.D.)
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany; (E.A.B.); (N.S.); (C.Z.); (J.S.K.); (T.B.); (M.D.)
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