基于深度学习的高分辨率相位对比图像中亚细胞器的分割。

IF 2 4区 生物学 Q4 CELL BIOLOGY
Cell structure and function Pub Date : 2024-08-30 Epub Date: 2024-07-31 DOI:10.1247/csf.24036
Kentaro Shimasaki, Yuko Okemoto-Nakamura, Kyoko Saito, Masayoshi Fukasawa, Kaoru Katoh, Kentaro Hanada
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引用次数: 0

摘要

虽然生物图像的定量分析需要精确提取特定的细胞器或细胞,但在宽视场灰度图像中,由于复杂的图像特征,传统的阈值分析方法一直受到阻碍,因此定量分析仍然具有挑战性。然而,快速发展的人工智能技术正在克服这些障碍。我们曾报道过微调的光栅化相位对比显微镜系统,可捕捉未染色活细胞中细胞器动态的高分辨率无标记图像(Shimasaki, K. et al. (2024).Cell Struct.Funct.,49:21-29)。我们在此展示了基于机器学习的相位对比图像亚细胞目标对象分割模型,该模型使用荧光标记作为地面实况掩膜的起源。这种方法能在高分辨率相位对比图像中准确分割细胞器,为研究未染色活细胞的细胞动力学提供了一个实用框架:无标签成像 细胞器动力学 光栅化相位对比 基于深度学习的分割
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep 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 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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来源期刊
Cell structure and function
Cell structure and function 生物-细胞生物学
CiteScore
2.50
自引率
0.00%
发文量
6
审稿时长
>12 weeks
期刊介绍: Cell Structure and Function is a fully peer-reviewed, fully Open Access journal. As the official English-language journal of the Japan Society for Cell Biology, it is published continuously online and biannually in print. Cell Structure and Function publishes important, original contributions in all areas of molecular and cell biology. The journal welcomes the submission of manuscripts on research areas such as the cell nucleus, chromosomes, and gene expression; the cytoskeleton and cell motility; cell adhesion and the extracellular matrix; cell growth, differentiation and death; signal transduction; the protein life cycle; membrane traffic; and organelles.
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