Temporal and Locational Values of Images Affecting the Deep Learning of Cancer Stem Cell Morphology

• Published in: Biomedicines Vol. 10; no. 5; p. 941
• Main Authors: Hanai, Yumi, Ishihata, Hiroaki, Zhang, Zaijun, Maruyama, Ryuto, Kasai, Tomonari, Kameda, Hiroyuki, Sugiyama, Tomoyasu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.04.2022; MDPI
• Subjects: Artificial intelligence; cancer stem cell; Cell culture; Cell morphology; classification; Cytology; Datasets; Deep learning; Linux; Lung cancer; Microscopy; Morphology; Neural networks; Physical characteristics; Pluripotency; R&D; Research & development; segmentation; Stem cell transplantation; Stem cells; Variance analysis; United States > US; Japan; segmentation; cell morphology; cancer stem cell; classification; artificial intelligence
• ISSN: 2227-9059; 2227-9059
• DOI: 10.3390/biomedicines10050941

Deep learning is being increasingly applied for obtaining digital microscopy image data of cells. Well-defined annotated cell images have contributed to the development of the technology. Cell morphology is an inherent characteristic of each cell type. Moreover, the morphology of a cell changes during its lifetime because of cellular activity. Artificial intelligence (AI) capable of recognizing a mouse-induced pluripotent stem (miPS) cell cultured in a medium containing Lewis lung cancer (LLC) cell culture-conditioned medium (cm), miPS-LLCcm cell, which is a cancer stem cell (CSC) derived from miPS cell, would be suitable for basic and applied science. This study aims to clarify the limitation of AI models constructed using different datasets and the versatility improvement of AI models. The trained AI was used to segment CSC in phase-contrast images using conditional generative adversarial networks (CGAN). The dataset included blank cell images that were used for training the AI but they did not affect the quality of predicting CSC in phase contrast images compared with the dataset without the blank cell images. AI models trained using images of 1-day culture could predict CSC in images of 2-day culture; however, the quality of the CSC prediction was reduced. Convolutional neural network (CNN) classification indicated that miPS-LLCcm cell image classification was done based on cultivation day. By using a dataset that included images of each cell culture day, the prediction of CSC remains to be improved. This is useful because cells do not change the characteristics of stem cells owing to stem cell marker expression, even if the cell morphology changes during culture.