4.2 Article

Effectiveness of Create ML in microscopy image classifications: a simple and inexpensive deep learning pipeline for non-data scientists

期刊

CHROMOSOME RESEARCH
卷 29, 期 3-4, 页码 361-371

出版社

SPRINGER
DOI: 10.1007/s10577-021-09676-z

关键词

Machine learning; deep learning; mitotic cell; chromosome; tetrad; microscope

资金

  1. JSPS KAKENHI [19H00937]
  2. Joint Usage/Research Center, Institute of Plant Science and Resources, Okayama University [2838, 2839, R240, IP2019]
  3. NIBB Collaborative Research Program [20-328]
  4. Grants-in-Aid for Scientific Research [19H00937] Funding Source: KAKEN

向作者/读者索取更多资源

This paper presents a system for chromosomal image classification using deep learning artificial intelligence, which is easy to apply to samples and has the advantage of low cost. Through training with limited samples, the system can accurately classify images containing mitotic cells, suitable for samples from different plant species and tissue types.
Observing chromosomes is a time-consuming and labor-intensive process, and chromosomes have been analyzed manually for many years. In the last decade, automated acquisition systems for microscopic images have advanced dramatically due to advances in their controlling computer systems, and nowadays, it is possible to automatically acquire sets of tiling-images consisting of large number, more than 1000, of images from large areas of specimens. However, there has been no simple and inexpensive system to efficiently select images containing mitotic cells among these images. In this paper, a classification system of chromosomal images by deep learning artificial intelligence (AI) that can be easily handled by non-data scientists was applied. With this system, models suitable for our own samples could be easily built on a Macintosh computer with Create ML. As examples, models constructed by learning using chromosome images derived from various plant species were able to classify images containing mitotic cells among samples from plant species not used for learning in addition to samples from the species used. The system also worked for cells in tissue sections and tetrads. Since this system is inexpensive and can be easily trained via deep learning using scientists' own samples, it can be used not only for chromosomal image analysis but also for analysis of other biology-related images.

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