Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells

Dai Kusumoto, Mark Lachmann, Takeshi Kunihiro, Shinsuke Yuasa, Yoshikazu Kishino, Mai Kimura, Toshiomi Katsuki, Shogo Itoh, Tomohisa Seki, Keiichi Fukuda

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Deep learning technology is rapidly advancing and is now used to solve complex problems. Here, we used deep learning in convolutional neural networks to establish an automated method to identify endothelial cells derived from induced pluripotent stem cells (iPSCs), without the need for immunostaining or lineage tracing. Networks were trained to predict whether phase-contrast images contain endothelial cells based on morphology only. Predictions were validated by comparison to immunofluorescence staining for CD31, a marker of endothelial cells. Method parameters were then automatically and iteratively optimized to increase prediction accuracy. We found that prediction accuracy was correlated with network depth and pixel size of images to be analyzed. Finally, K-fold cross-validation confirmed that optimized convolutional neural networks can identify endothelial cells with high performance, based only on morphology. Kusumoto et al. developed an automated system to identify endothelial cells derived from induced pluripotent stem cells, based only on morphology. Performance, as assessed by F1 score and accuracy, was correlated with network depth and pixel size of training images. K-fold validation confirmed that endothelial cells are identified automatically with high accuracy using only generalized morphological features.

Original languageEnglish
Pages (from-to)1687-1695
Number of pages9
JournalStem Cell Reports
Volume10
Issue number6
DOIs
Publication statusPublished - 2018 Jun 5

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Keywords

  • artificial intelligence
  • deep learning
  • endothelial cell
  • induced pluripotent stem cell
  • machine learning

ASJC Scopus subject areas

  • Biochemistry
  • Genetics
  • Developmental Biology
  • Cell Biology

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