Deep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images.

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

SUBMITTER: Shimasaki K 

PROVIDER: S-EPMC11930775 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature