AI Article Synopsis
- Traditional methods for analyzing biological images struggle with complex features, making it tough to extract specific organelles or cells from broad-field grayscale images.
- A new apodized phase-contrast microscopy system captures high-resolution, label-free images of living cells, allowing for observation of organelle dynamics without staining.
- Machine learning-based segmentation models have been developed to accurately identify organelles in these images, offering a valuable tool for studying cellular processes in real time.
Article 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 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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| http://dx.doi.org/10.1247/csf.24036 | DOI Listing |
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