Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology

29 Nov 2019

Abstract excerpt

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally-restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here we introduce QuantiMus, a free software program that...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Artificial Intelligence
Share to Biomarkers
Share to Image Processing, Computer-Assisted

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Magnetic Resonance Imaging

Share to Mice, Inbred C57BL

Share to Muscle Fibers, Skeletal

Share to Phenotype

Share to Workflow

Source

Identifiers

PMID: 31849692
DOI: 10.3389/fphys.2019.01416

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle MorphologyPMID 31849692Read ↗

Automated Image Analysis Pipeline Development to Monitor Disease Progression in Muscular Dystrophy Using CellProfilerSemantic proximity Read ↗

FLASH: Simultaneous Muscle Fiber Typing and Morphometry AnalysisSemantic proximity Read ↗

Automatic and unbiased segmentation and quantification of myofibers in skeletal muscleSemantic proximity Read ↗

High-throughput data-driven analysis of myofiber composition reveals muscle-specific disease and age-associated patternsSemantic proximity Read ↗

Artificial intelligence workflow quantifying muscle features on Hematoxylin-Eosin stained sections reveals dystrophic phenotype amelioration upon treatment.Semantic proximity Read ↗

NMJ-Analyser: high-throughput morphological screening of neuromuscular junctions identifies subtle changes in mouse neuromuscular disease modelsSemantic proximity Read ↗

Distribution of MRI-derived T2 values as a biomarker for in vivo rapid screening of phenotype severity in mdx mice.Semantic proximity Read ↗

In situ spatial transcriptomic analysis of human skeletal muscle using the Xenium platformSemantic proximity Read ↗

Histological Methods to Assess Skeletal Muscle Degeneration and Regeneration in Duchenne Muscular DystrophySemantic proximity Read ↗

MyoCount: a software tool for the automated quantification of myotube surface area and nuclear fusion indexSemantic proximity Read ↗

Human Skeletal Muscle Fiber Heterogeneity Beyond Myosin Heavy ChainsSemantic proximity Read ↗

A machine learning-based clinical tool for diagnosing myopathy using multi-cohort microarray expression profilesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.