Evidence map›Paper›PMID 40911055›Full record

ArticleHistochemistry and cell biology2025

MyoAnalyst: an ImageJ plugin for accurate and automatic myofiber segmentation and analysis in skeletal muscle cross sections.

Bao Zhang, Shuaiyu Wang, Yaning Wang, Chen Liang, Hongbo Zhang

Abstract read
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In one paragraph

Article in Histochemistry and cell biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. October in focus in HCB.Histochemistry and cell biology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Bao ZhangMedical College of Jiaying University, Meizhou, 514031, China.
Shuaiyu WangDepartment of Obstetrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Yaning WangThe Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Chen LiangThe Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Hongbo ZhangThe Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China. zhanghongbo@mail.sysu.edu.cn.

Funding

Fundamental Research Funds for the Central Universities, Sun Yat-sen University 24ykzy002National Natural Science Foundation of China 31871370National Natural Science Foundation of China 32400685
6 · The paper itself

Abstract

Quantifying myofiber size is essential for assessing the health and function of skeletal muscle. Although several ImageJ plugins are currently available for myofiber segmentation and size quantification, significant challenges remain-most notably limited accuracy and poor compatibility with hematoxylin and eosin (H&E)-stained skeletal muscle cross sections. In this study, we introduce MyoAnalyst, an ImageJ plugin designed to enable automated analysis of both immunofluorescence (IF)- and H&E-stained skeletal muscle cross sections. Compared to existing ImageJ plugins, MyoAnalyst delivers enhanced segmentation sensitivity and superior boundary delineation accuracy across both healthy and injured muscle tissue stained with IF. Importantly, it also supports fully automated analysis of H&E-stained sections. With its intuitive graphical interface and batch processing capabilities, MyoAnalyst provides a potentially efficient tool for myofiber size quantification in both research and clinical settings.

Indexed as

Image Processing, Computer-AssistedMuscle Fibers, SkeletalMuscle, SkeletalSoftwareAnimalsAutomationMiceAutomated analysisMyoAnalystMyofiber segmentationSkeletal muscle cross section

Identifiers

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Registered trials

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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.