Development of a deep learning based image processing tool for enhanced organoid analysis

• Published in: Scientific reports Vol. 13; no. 1; p. 19841
• Main Authors: Park, Taeyun, Kim, Taeyul K., Han, Yoon Dae, Kim, Kyung-A, Kim, Hwiyoung, Kim, Han Sang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.11.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/1647; Artificial intelligence; Cell culture; Deep Learning; Humanities and Social Sciences; Humans; Image processing; Image Processing, Computer-Assisted; Internal medicine; Medical schools; Medicine; Morphology; multidisciplinary; Organoids; Recognition, Psychology; Research Personnel; Researchers; Science; Science (multidisciplinary); Stem cells; Subculture; University colleges
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-46485-2

Contrary to 2D cells, 3D organoid structures are composed of diverse cell types and exhibit morphologies of various sizes. Although researchers frequently monitor morphological changes, analyzing every structure with the naked eye is difficult. Given that deep learning (DL) has been used for 2D cell image segmentation, a trained DL model may assist researchers in organoid image recognition and analysis. In this study, we developed OrgaExtractor, an easy-to-use DL model based on multi-scale U-Net, to perform accurate segmentation of organoids of various sizes. OrgaExtractor achieved an average dice similarity coefficient of 0.853 from a post-processed output, which was finalized with noise removal. Correlation between CellTiter-Glo assay results and daily measured organoid images shows that OrgaExtractor can reflect the actual organoid culture conditions. The OrgaExtractor data can be used to determine the best time point for organoid subculture on the bench and to maintain organoids in the long term.