2019
DOI: 10.1186/s13395-019-0200-7
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Automated image-analysis method for the quantification of fiber morphometry and fiber type population in human skeletal muscle

Abstract: Background The quantitative analysis of muscle histomorphometry has been growing in importance in both research and clinical settings. Accurate and stringent assessment of myofibers’ changes in size and number, and alterations in the proportion of oxidative (type I) and glycolytic (type II) fibers is essential for the appropriate study of aging and pathological muscle, as well as for diagnosis and follow-up of muscle diseases. Manual and semi-automated methods to assess muscle morphometry in secti… Show more

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Cited by 36 publications
(44 citation statements)
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“…When coupled with high-throughput slide scanning to generate whole slide images (WSI), in both brightfield and fluorescent paradigms, digital image analysis facilitates the assessment of entire tissue landscapes, alleviating potential bias from manual selection of discreet regions of interest for analysis, and enabling upscaled assessment of fibres in a biopsy from a few hundred to many thousands. Subsequent image analysis using modern tissue phenomics software can unlock unparalleled multiparametric data analytics for biological tissues [7,11,23,37]. Alongside diagnostics, there is also a growing interest in the use of automated digital scripts for the analysis of pathological end-points and/or outcome measures in clinical trial samples [35].…”
Section: Introductionmentioning
confidence: 99%
“…When coupled with high-throughput slide scanning to generate whole slide images (WSI), in both brightfield and fluorescent paradigms, digital image analysis facilitates the assessment of entire tissue landscapes, alleviating potential bias from manual selection of discreet regions of interest for analysis, and enabling upscaled assessment of fibres in a biopsy from a few hundred to many thousands. Subsequent image analysis using modern tissue phenomics software can unlock unparalleled multiparametric data analytics for biological tissues [7,11,23,37]. Alongside diagnostics, there is also a growing interest in the use of automated digital scripts for the analysis of pathological end-points and/or outcome measures in clinical trial samples [35].…”
Section: Introductionmentioning
confidence: 99%
“…The macro also generates size-based (CSA or major/minor diameter) color-coded section maps, which, as the authors demonstrate, make it simple to visually detect differences in CSA distributions between different tissues. However, it is only built to handle two fiber types, and as such is not able to distinguish Type II subtypes from one another or identify mixed fiber types [39]. As a novel alternative, we employed a machine learning-based approach to improve the accuracy of image segmentation without altering standard protocols for tissue staining or image acquisition.…”
Section: Discussionmentioning
confidence: 99%
“…Decreases in MYH2 and MYH7 in older individuals resemble those seen for the soleus muscle [39] and suggest losses of MYH2 and MYH7 as causes of age‐dependent muscle fibre atrophy in the OOM. Although muscle fibres shift its type depending on muscle species and stimulations [39‐41], decreased MYH2 and MYH7 in combination with a constant MYH4 level suggests oxidative‐ to glycolytic‐type changes in OOM with age, because MYH2, −4 and −7 are myosin heavy chains for fast‐twitch oxidative glycolytic muscle fibres, fast‐twitch glycolytic muscle fibres and slow‐twitch oxidative fibres, respectively.…”
Section: Discussionmentioning
confidence: 99%