Evaluation of Image Classification for Quantifying Mitochondrial Morphology Using Deep Learning.

Journal: Endocrine, metabolic & immune disorders drug targets
PMID: 35786342

Abstract

BACKGROUND: Mitochondrial morphology reversibly changes between fission and fusion. As these changes (mitochondrial dynamics) reflect the cellular condition, they are one of the simplest indicators of cell state and predictors of cell fate. However, it is currently difficult to classify them using a simple and objective method.

Authors

  • Kaori Tsutsumi
    Faculty of Health Sciences, Hokkaido University, Sapporo, Japan.
  • Keima Tokunaga
    Department of Health Sciences, School of Medicine, Hokkaido University, Sapporo, Japan.
  • Shun Saito
    Department of Radiology, Kyorin University Hospital, 6-20-2 Shinkawa, Mitaka-shi, Tokyo, 181-8611, Japan.
  • Tatsuya Sasase
    Graduate School of Health Sciences, Hokkaido University, Sapporo, Japan.
  • Hiroyuki Sugimori
    Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan.

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