Deep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images.
Cell Struct Funct
; 2024 Jul 31.
Article
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| MEDLINE
| ID: mdl-39085139
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., 4921-29). 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 segmentation of organelles in high-resolution phase-contrast images, providing a practical framework for studying cellular dynamics in unstained living cells.Key words Label-free imaging, Organelle dynamics, Apodized phase contrast, Deep learning-based segmentation.
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01-internacional
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MEDLINE
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En
Revista:
Cell Struct Funct
Año:
2024
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Article