start-ver=1.4
cd-journal=joma
no-vol=29
cd-vols=
no-issue=3-4
article-no=
start-page=361
end-page=371
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2021
dt-pub=20211014
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Effectiveness of Create ML in microscopy image classifications: a simple and inexpensive deep learning pipeline for non-data scientists
en-subtitle=
kn-subtitle=
en-abstract=
kn-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.
en-copyright=
kn-copyright=

en-aut-name=NagakiKiyotaka
en-aut-sei=Nagaki
en-aut-mei=Kiyotaka
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=FurutaTomoyuki
en-aut-sei=Furuta
en-aut-mei=Tomoyuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=YamajiNaoki
en-aut-sei=Yamaji
en-aut-mei=Naoki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

en-aut-name=KuniyoshiDaichi
en-aut-sei=Kuniyoshi
en-aut-mei=Daichi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=

en-aut-name=IshiharaMegumi
en-aut-sei=Ishihara
en-aut-mei=Megumi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=

en-aut-name=KishimaYuji
en-aut-sei=Kishima
en-aut-mei=Yuji
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=6
ORCID=

en-aut-name=MurataMinoru
en-aut-sei=Murata
en-aut-mei=Minoru
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=7
ORCID=

en-aut-name=HoshinoAtsushi
en-aut-sei=Hoshino
en-aut-mei=Atsushi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=8
ORCID=

en-aut-name=TakatsukaHirotomo
en-aut-sei=Takatsuka
en-aut-mei=Hirotomo
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=9
ORCID=

affil-num=1
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=

affil-num=2
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=

affil-num=3
en-affil=Institute of Plant Science and Resources, Okayama University
kn-affil=

affil-num=4
en-affil=Laboratory of Plant Breeding, Research Faculty of Agriculture, Hokkaido University
kn-affil=

affil-num=5
en-affil=Laboratory of Plant Breeding, Research Faculty of Agriculture, Hokkaido University
kn-affil=

affil-num=6
en-affil=Laboratory of Plant Breeding, Research Faculty of Agriculture, Hokkaido University
kn-affil=

affil-num=7
en-affil=Department of Agricultural and Food Science, Universiti Tunku Abdul Rahman
kn-affil=

affil-num=8
en-affil=National Institute for Basic Biology
kn-affil=

affil-num=9
en-affil=Graduate School of Science and Technology, Nara Institute of Science and Technology
kn-affil=
en-keyword=Machine learning
kn-keyword=Machine learning
en-keyword=deep learning
kn-keyword=deep learning
en-keyword=mitotic cell
kn-keyword=mitotic cell
en-keyword=chromosome
kn-keyword=chromosome
en-keyword=tetrad
kn-keyword=tetrad
en-keyword=microscope
kn-keyword=microscope
END