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2018 Fiscal Year Annual Research Report

Automatic Detection of Good/Bad Colonies of iPS Cells Using Deep Learning

Research Project

Project/Area Number 16K00394
Research InstitutionHiroshima University

Principal Investigator

ライチェフ ビセル  広島大学, 工学研究科, 准教授 (00531922)

Project Period (FY) 2016-04-01 – 2019-03-31
KeywordsiPS cells / Machine learning / Neural networks
Outline of Annual Research Achievements

Induced pluripotent stem (iPS) cells have shown a huge potential for the development of regenerative medicine. However, since a large number of undifferentiated human iPS cells must be prepared for use as a renewable source of replacement cells for regenerative medicine, the development of an automated culture system for iPS cells is considered very important. In such a system, detecting the anomalies which can arise during the culture process is considered to be crucial. In this research project we have designed a novel machine learning based method for automatic detection of Good (undifferentiated) vs. Bad (differentiated) colonies of iPS cells directly from images, which makes possible the automation of the cell harvesting process.

The proposed method is based on deep learning using a Convolutional Neural Network (CNN) with a modified architecture which is better able to capture the relevant structural information regarding the spatial distribution of class probabilities. Additionally, the training can be accomplished using only a small labeled training dataset. Experimental results show that very good accuracy can be achieved by the proposed method, outperforming other current state-of-the-art algorithms for biomedical image analysis.

  • Research Products

    (4 results)

All 2018

All Presentation (4 results) (of which Int'l Joint Research: 1 results,  Invited: 2 results)

  • [Presentation] Patch-based learning for biomedical image analysis2018

    • Author(s)
      Bisser Raytchev
    • Organizer
      Hiroshima Conference on Statistical Science 2016
    • Int'l Joint Research / Invited
  • [Presentation] Detection of Differentiated vs. Undifferentiated Colonies of iPS Cells Using Random Forests Modeled with the Multivariate Polya Distribution2018

    • Author(s)
      Bisser Raytchev
    • Organizer
      第20回 画像の認識・理解シンポジウム(MIRU2017)
    • Invited
  • [Presentation] 構造情報を用いた分類型CNNによるiPS細胞の分化・未分化検出2018

    • Author(s)
      林 祥平, Bisser Raytchev, 玉木 徹, 金田 和文
    • Organizer
      第21回 画像の認識・理解シンポジウム(MIRU2018)
  • [Presentation] Multi-Scale Scene Recognition Using Small Training Datasets2018

    • Author(s)
      Tokinirina Radiniaina, Bisser Raytchev, Kazufumi Kaneda, Toru Tamaki
    • Organizer
      第21回 画像の認識・理解シンポジウム(MIRU2018)

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Published: 2019-12-27  

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