RRC ID 69152
Author Nagaki K, Furuta T, Yamaji N, Kuniyoshi D, Ishihara M, Kishima Y, Murata M, Hoshino A, Takatsuka H.
Title Effectiveness of Create ML in microscopy image classifications: a simple and inexpensive deep learning pipeline for non-data scientists.
Journal Chromosome Res
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.
Volume 29(3-4)
Pages 361-371
Published 2021-12-1
DOI 10.1007/s10577-021-09676-z
PII 10.1007/s10577-021-09676-z
PMID 34648121
MeSH Artificial Intelligence Deep Learning* Image Processing, Computer-Assisted Microscopy
IF 3.413
Morning Glory AK1