ArticlePloS one2020
Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- MyoAnalyst: an ImageJ plugin for accurate and automatic myofiber segmentation and analysis in skeletal muscle cross sections.Histochemistry and cell biology · 2025Article
- Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.Communications medicine · 2025Article
- Across the space: applications of spatial transcriptomic technology in healthy and diseased muscle.Frontiers in cell and developmental biology · 2025Review
- From Muscle-Bone Concept to the ArthroMyoFascial Complex: A Pragmatic Anatomical Concept for Physiotherapy and Manual Therapy.Life (Basel, Switzerland) · 2024Review
- Article
- MyoV: a deep learning-based tool for the automated quantification of muscle fibers.Briefings in bioinformatics · 2024Article
- Pro-fibrogenic and adipogenic aspects of chronic muscle degeneration are contributed by distinct stromal cell subpopulations.PloS one · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger's line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed segmentation. Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~92.4%. The software has been tested to work on full muscle cross-sections and works well even under non-optimal staining conditions. The MyoSAT software tool has been implemented as a macro for the freely available ImageJ software platform. This new segmentation tool allows scientists to efficiently analyze large muscle cross-sections for use in research studies and diagnostics.
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What OpenQuestion holds
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.