FiNuTyper: Design and validation of an automated deep learning-based platform for simultaneous fiber and nucleus type analysis in human skeletal muscle.

Journal: Acta physiologica (Oxford, England)
PMID: 37097015

Abstract

AIM: While manual quantification is still considered the gold standard for skeletal muscle histological analysis, it is time-consuming and prone to investigator bias. To address this challenge, we assembled an automated image analysis pipeline, FiNuTyper (Fiber and Nucleus Typer).

Authors

  • August Lundquist
    Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
  • Enikő Lázár
    Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
  • Nan S Han
    Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
  • Eric B Emanuelsson
    Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
  • Stefan M Reitzner
    Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
  • Mark A Chapman
    Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
  • Vera Shirokova
    Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
  • Kanar Alkass
    Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
  • Henrik Druid
    Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
  • Susanne Petri
    Department of Neurology, Hanover Medical School, Hanover, Germany.
  • Carl J Sundberg
    Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
  • Olaf Bergmann
    Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.

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