{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["49(2)"],"submitter":["Shimasaki K"],"pubmed_abstract":["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 segmentatio"],"journal":["Cell structure and function"],"pagination":["57-65"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11930775"],"repository":["biostudies-literature"],"pubmed_title":["Deep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images."],"pmcid":["PMC11930775"],"pubmed_authors":["Katoh K","Saito K","Fukasawa M","Shimasaki K","Okemoto-Nakamura Y","Hanada K"],"additional_accession":[]},"is_claimable":false,"name":"Deep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images.","description":"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 segmentatio","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Aug","modification":"2025-07-05T03:04:21.069Z","creation":"2025-07-05T03:04:21.069Z"},"accession":"S-EPMC11930775","cross_references":{"pubmed":["39085139"],"doi":["10.1247/csf.24036"]}}