ArticleJournal of applied physiology (Bethesda, Md. : 1985)2013
Automated image analysis of skeletal muscle fiber cross-sectional area.
Article in Journal of applied physiology (Bethesda, Md. : 1985), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
41 citing papers in PubMed, 63 citations in OpenAlex.
- Focused Electric Field Technology: A Novel Myoelectrical Stimulation Technology for Noninvasive Aging Muscle Rejuvenation.Journal of cosmetic dermatology · 2025Trial
- PathViT Model for Automated Disease Classification from Skeletal Muscle Histopathology.The American journal of pathology · 2026Article
- FibroTrack: a standalone deep learning platform for automated fibrosis quantification in muscle and cardiac histology.Skeletal muscle · 2026Article
- MyoQuant: An optimized image analysis algorithm for quantitative analysis of skeletal muscle fibers.MethodsX · 2025Article
- AFTG-Net: A Deep Attention-based Fusion Framework of Topological and Gradient Features for Pathological Image Analysis.Research square · 2025Article
- KL-Biome (Postbiotic Formulation ofInternational journal of molecular sciences · 2024Article
- Muscle fibre morphometric analysis (MusMA) correlates with muscle function and cardiovascular risk prognosis.International journal of experimental pathology · 2024Article
- Dexamethasone-induced muscle atrophy and bone loss in six genetically diverse collaborative cross founder strains demonstrates phenotypic variability by Rg3 treatment.Journal of ginseng research · 2024Article
- Article
- Skeletal muscle adaptations to high-intensity, low-volume concurrent resistance and interval training in recreationally active men and women.Physiological reports · 2024Article
- DOCK3 regulates normal skeletal muscle regeneration and glucose metabolism.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2023Article
- Artificial PGA/Collagen-based Bilayer Conduit in Short Gap Interposition Setting Provides Comparable Regenerative Potential to Direct Suture.Plastic and reconstructive surgery. Global open · 2023Article
- DOCK3 regulates normal skeletal muscle regeneration and glucose metabolism.bioRxiv : the preprint server for biology · 2023Article
- Histological Methods to Assess Skeletal Muscle Degeneration and Regeneration in Duchenne Muscular Dystrophy.International journal of molecular sciences · 2022Review
- Ornithine Aspartate and Vitamin-E Combination Has Beneficial Effects on Cardiovascular Risk Factors in an Animal Model of Nonalcoholic Fatty Liver Disease in Rats.Biomolecules · 2022Article
- 3D synchrotron imaging of muscle tissues at different atrophic stages in stroke and spinal cord injury: a proof-of-concept study.Scientific reports · 2022Article
- Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.Arthritis research & therapy · 2022Review
- A User-Friendly Approach for Routine Histopathological and Morphometric Analysis of Skeletal Muscle Using CellProfiler Software.Diagnostics (Basel, Switzerland) · 2022Article
- Automated image segmentation method to analyse skeletal muscle cross section in exercise-induced regenerating myofibers.Scientific reports · 2021Article
- Computer-aided diagnosis in rheumatic diseases using ultrasound: an overview.Clinical rheumatology · 2020Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Morphological characteristics of muscle fibers, such as fiber size, are critical factors that determine the health and function of the muscle. However, at this time, quantification of muscle fiber cross-sectional area is still a manual or, at best, a semiautomated process. This process is labor intensive, time consuming, and prone to errors, leading to high interobserver variability. We have developed and validated an automatic image segmentation algorithm and compared it directly with commercially available semiautomatic software currently considered state of the art. The proposed automatic segmentation algorithm was evaluated against a semiautomatic method with manual annotation using 35 randomly selected cross-sectional muscle histochemical images. The proposed algorithm begins with ridge detection to enhance the muscle fiber boundaries, followed by robust seed detection based on concave area identification to find initial seeds for muscle fibers. The final muscle fiber boundaries are automatically delineated using a gradient vector flow deformable model. Our automatic approach is accurate and represents a significant advancement in efficiency; quantification of fiber area in muscle cross sections was reduced from 25-40 min/image to 15 s/image, while accommodating common quantification obstacles including morphological variation (e.g., heterogeneity in fiber size and fibrosis) and technical artifacts (e.g., processing defects and poor staining quality). Automatic quantification of muscle fiber cross-sectional area using the proposed method is a powerful tool that will increase sensitivity, objectivity, and efficiency in measuring muscle adaptation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.