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

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Abstract

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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Data availability

The models created in this study can be downloaded from GuiHub (https://github.com/tomoyukif/nagakiCreateMLmodels).

Code availability

CutSort is freely available on GitHub (https://github.com/tomoyukif/CutSort).

Abbreviations

Asa:

Allium sativum

Ace:

Allium cepa

Afi:

Allium fistulosum

AI:

Artificial intelligence

At:

Arabidopsis thaliana

Asa:

Allium sativum

AtCell:

Arabidopsis thaliana Cultured cell

Atu:

Allium tuberosum

BY-2:

Nicotiana tabacum Cultured cell line BY-2

CLI:

Command line interface

Eg:

Elaeis guineensis

GUI:

Graphical user interface

Ha:

Helianthus annuus

IC:

Image classifier

IC (+ op):

Image classifier with options

Ini:

Ipomoea nil

Je:

Juncus effusus

Ln:

Luzula nivea

Mw:

Microcoelum weddelliana

Ns:

Nicotiana sylvestris

Nt:

Nicotiana tabacum

Nto:

Nicotiana tomentosiformis

OD:

Object detector

Os:

Oryza sativa

OsCell:

Oryza sativa Cultured cell

PNG:

Portable network graphics

So:

Saccharum officinarum

Ta:

Triticum aestivum

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Acknowledgements

I. nil seeds were provided by the National BioResource Project (NBRP). The seeds of N. tabacum, N. sylvestris, and N. tomentosiformis were gifts from Japan Tobacco, Inc. The stems of S. officinarum were gifts from the Japan International Research Center for Agricultural Sciences.

Funding

This work was partly supported by grants from JSPS KAKENHI (No. 19H00937 to Yuji Kishima), the Joint Usage/Research Center, Institute of Plant Science and Resources, Okayama University (Nos. 2838 to Hirotomo Takatsuka, 2839 to Atsushi Hoshino, R240 to Yuji Kishima, and IP2019 to Minoru Murata), and the NIBB Collaborative Research Program (20–328 to Atsushi Hoshino).

Author information

Authors and Affiliations

  1. Institute of Plant Science and Resources, Okayama University, Kurashiki, 710-0046, Japan

    Kiyotaka Nagaki, Tomoyuki Furuta & Naoki Yamaji

  2. Laboratory of Plant Breeding, Research Faculty of Agriculture, Hokkaido University, Sapporo, 060-8589, Japan

    Daichi Kuniyoshi, Megumi Ishihara & Yuji Kishima

  3. Department of Agricultural and Food Science, Universiti Tunku Abdul Rahman, 31900, Kampar, Perak, Malaysia

    Minoru Murata

  4. National Institute for Basic Biology, Okazaki, 444-8585, Japan

    Atsushi Hoshino

  5. Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, 444-8585, Japan

    Atsushi Hoshino

  6. Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan

    Hirotomo Takatsuka

  7. School of Biological Science and Technology, College of Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan

    Hirotomo Takatsuka

Authors
  1. Kiyotaka Nagaki
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  2. Tomoyuki Furuta
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  3. Naoki Yamaji
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  4. Daichi Kuniyoshi
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  5. Megumi Ishihara
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  6. Yuji Kishima
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  7. Minoru Murata
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  8. Atsushi Hoshino
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  9. Hirotomo Takatsuka
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Contributions

Kiyotaka Nagaki conceived the study and conducted the experiments except the CLSM imaging and the preparation and capture of the I. nil, A. thaliana, E. guineensis, and S. weddelliana chromosome images and tetrad images; performed the deep learning; produced all the figures; and wrote the manuscript. Tomoyuki Furuta developed the sorting application and reviewed the manuscript. Naoki Yamaji conducted the CLSM imaging. Daichi Kuniyoshi, Megumi Ishihara, and Yuji Kishima conducted the tetrad analysis. Atsushi Hoshino (I. nil), Hirotomo Takatsuka (A. thaliana), and Minoru Murata (E. guineensis and S. weddelliana) prepared and captured the chromosome images.

Corresponding author

Correspondence to Kiyotaka Nagaki.

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Nagaki, K., Furuta, T., Yamaji, N. et al. Effectiveness of Create ML in microscopy image classifications: a simple and inexpensive deep learning pipeline for non-data scientists. Chromosome Res 29, 361–371 (2021). https://doi.org/10.1007/s10577-021-09676-z

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  • DOI: https://doi.org/10.1007/s10577-021-09676-z

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