biostudies-literatureUnknown49(2)Shimasaki KAlthough quantitative analysis of biological images demands precise extraction of specific organelles or cells, it remains challenging in broad-field grayscale images, where traditional thresholding methods have been hampered due to complex image features. Nevertheless, rapidly growing artificial intelligence technology is overcoming obstacles. We previously reported the fine-tuned apodized phase-contrast microscopy system to capture high-resolution, label-free images of organelle dynamics in unstained living cells (Shimasaki, K. et al. (2024). Cell Struct. Funct., 49: 21-29). We here showed machine learning-based segmentation models for subcellular targeted objects in phase-contrast images using fluorescent markers as origins of ground truth masks. This method enables accurate segmentatioCell structure and function57-65https://www.ebi.ac.uk/biostudies/studies/S-EPMC11930775biostudies-literatureDeep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images.PMC11930775Katoh KSaito KFukasawa MShimasaki KOkemoto-Nakamura YHanada KfalseDeep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images.Although quantitative analysis of biological images demands precise extraction of specific organelles or cells, it remains challenging in broad-field grayscale images, where traditional thresholding methods have been hampered due to complex image features. Nevertheless, rapidly growing artificial intelligence technology is overcoming obstacles. We previously reported the fine-tuned apodized phase-contrast microscopy system to capture high-resolution, label-free images of organelle dynamics in unstained living cells (Shimasaki, K. et al. (2024). Cell Struct. Funct., 49: 21-29). We here showed machine learning-based segmentation models for subcellular targeted objects in phase-contrast images using fluorescent markers as origins of ground truth masks. This method enables accurate segmentatio2024-01-01T00:00:00Z2024 Aug2025-07-05T03:04:21.069Z2025-07-05T03:04:21.069ZS-EPMC119307753908513910.1247/csf.24036