論文

査読有り 国際誌
2020年7月16日

Deep learning-based classification of the mouse estrous cycle stages.

Scientific reports
  • Kyohei Sano
  • ,
  • Shingo Matsuda
  • ,
  • Suguru Tohyama
  • ,
  • Daisuke Komura
  • ,
  • Eiji Shimizu
  • ,
  • Chihiro Sutoh

10
1
開始ページ
11714
終了ページ
11714
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1038/s41598-020-68611-0

There is a rapidly growing demand for female animals in preclinical animal, and thus it is necessary to determine animals' estrous cycle stages from vaginal smear cytology. However, the determination of estrous stages requires extensive training, takes a long time, and is costly; moreover, the results obtained by human examiners may not be consistent. Here, we report a machine learning model trained with 2,096 microscopic images that we named the "Stage Estimator of estrous Cycle of RodEnt using an Image-recognition Technique (SECREIT)." With the test dataset (736 images), SECREIT achieved area under the receiver-operating-characteristic curve of 0.962 or more for each estrous stage. A test using 100 images showed that SECREIT provided correct classification that was similar to that provided by two human examiners (SECREIT: 91%, Human 1: 91%, Human 2: 79%) in 11 s. The SECREIT can be a first step toward accelerating the research using female rodents.

リンク情報
DOI
https://doi.org/10.1038/s41598-020-68611-0
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/32678183
ID情報
  • DOI : 10.1038/s41598-020-68611-0
  • PubMed ID : 32678183

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