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明场染色与荧光染色数据集——基于机器学习的虚拟染色的测试床图像集。

Brightfield vs Fluorescent Staining Dataset-A Test Bed Image Set for Machine Learning based Virtual Staining.

机构信息

Institute for Fundamental Medicine and Biology, Kazan Federal University, Kazan, 420008, Russia.

Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University "LETI", St. Petersburg, 197022, Russia.

出版信息

Sci Data. 2023 Mar 22;10(1):160. doi: 10.1038/s41597-023-02065-7.

DOI:10.1038/s41597-023-02065-7
PMID:36949058
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10033900/
Abstract

Differential fluorescent staining is an effective tool widely adopted for the visualization, segmentation and quantification of cells and cellular substructures as a part of standard microscopic imaging protocols. Incompatibility of staining agents with viable cells represents major and often inevitable limitations to its applicability in live experiments, requiring extraction of samples at different stages of experiment increasing laboratory costs. Accordingly, development of computerized image analysis methodology capable of segmentation and quantification of cells and cellular substructures from plain monochromatic images obtained by light microscopy without help of any physical markup techniques is of considerable interest. The enclosed set contains human colon adenocarcinoma Caco-2 cells microscopic images obtained under various imaging conditions with different viable vs non-viable cells fractions. Each field of view is provided in a three-fold representation, including phase-contrast microscopy and two differential fluorescent microscopy images with specific markup of viable and non-viable cells, respectively, produced using two different staining schemes, representing a prominent test bed for the validation of image analysis methods.

摘要

差示荧光染色是一种广泛应用于细胞和细胞亚结构可视化、分割和定量的有效工具,是标准显微镜成像方案的一部分。由于染色剂与活细胞不兼容,这代表着它在活体实验中的应用存在重大且往往不可避免的限制,需要在实验的不同阶段提取样本,从而增加了实验室成本。因此,开发能够对细胞和细胞亚结构进行分割和定量的计算机化图像分析方法,从没有任何物理标记技术帮助的普通单色显微镜图像中获取,这是非常有意义的。随函附上的是在不同成像条件下用不同死活细胞比例获得的人结肠腺癌 Caco-2 细胞的显微镜图像。每个视场提供了三重表示,包括相差显微镜和两种具有特定标记的差示荧光显微镜图像,分别使用两种不同的染色方案获得,这代表了用于验证图像分析方法的重要测试平台。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eb2/10033900/0b18187c221c/41597_2023_2065_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eb2/10033900/ea93dc96bd25/41597_2023_2065_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eb2/10033900/8dbb2d1e2564/41597_2023_2065_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eb2/10033900/68d734b121c0/41597_2023_2065_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eb2/10033900/9f8b6d8f1b50/41597_2023_2065_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eb2/10033900/0b18187c221c/41597_2023_2065_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eb2/10033900/ea93dc96bd25/41597_2023_2065_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eb2/10033900/8dbb2d1e2564/41597_2023_2065_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eb2/10033900/68d734b121c0/41597_2023_2065_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eb2/10033900/9f8b6d8f1b50/41597_2023_2065_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eb2/10033900/0b18187c221c/41597_2023_2065_Fig5_HTML.jpg

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本文引用的文献

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EVICAN-a balanced dataset for algorithm development in cell and nucleus segmentation.EVICAN-用于细胞和细胞核分割算法开发的平衡数据集。
Bioinformatics. 2020 Jun 1;36(12):3863-3870. doi: 10.1093/bioinformatics/btaa225.
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The Quantitative-Phase Dynamics of Apoptosis and Lytic Cell Death.细胞凋亡和溶细胞性细胞死亡的定量相动力学。
Sci Rep. 2020 Jan 31;10(1):1566. doi: 10.1038/s41598-020-58474-w.
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Machine learning approach of automatic identification and counting of blood cells.血细胞自动识别与计数的机器学习方法。
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Fast and simple tool for the quantification of biofilm-embedded cells sub-populations from fluorescent microscopic images.一种快速简单的工具,可从荧光显微镜图像中定量生物膜嵌入细胞的亚群。
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