Deep Learning Approach for Quantification of Fluorescently Labeled Blood Cells in Danio rerio (Zebrafish).
Deep Learning Approach for Quantification of Fluorescently Labeled Blood Cells in Danio rerio (Zebrafish).
复制标题
深度学习方法，用于定量Danio Rerio（斑马鱼）中荧光标记的血细胞。
DOI：
10.1177/11779322211037770
复制
发表时间：
2021
影响因子：
5.8
通讯作者：
Stachura DL
中科院分区：
文献类型：
--
作者：
Thapa S;Stachura DL
中性粒细胞是一种白色血细胞，对先天免疫系统的功能至关重要。为了阐明中性粒细胞生物学的机制，在脊椎动物模型系统中进行了许多研究。在斑马鱼中，由于具有荧光标记的白细胞的转基因菌株，嗜中性粒细胞的体内成像是可能的。然而，由于中性粒细胞的相对丰度，计数过程是费力和主观的。在这篇文章中，我们提出了一个自定义训练的“你只看一次”（YOLO）机器学习算法，以自动识别和计数荧光标记的中性粒细胞在斑马鱼。使用该模型，我们发现人类计数与模型之间的相关系数为r = 0.8207，误差率为8.65%，而人类计数器之间的变异为5%至12%。重要的是，该模型能够正确验证先前发表的手动定量中性粒细胞的文章的结果。虽然准确性可以进一步提高，但与标准手动计数协议相比，该模型仅需几分钟即可实现这些结果，并且任何具有基本编程知识的人都可以执行。它进一步支持在斑马鱼模型系统中使用深度学习模型对荧光标记的血细胞进行高通量分析。
Neutrophils are a type of white blood cell essential for the function of the innate immune system. To elucidate mechanisms of neutrophil biology, many studies are performed in vertebrate animal model systems. In Danio rerio (zebrafish), in vivo imaging of neutrophils is possible due to transgenic strains that possess fluorescently labeled leukocytes. However, due to the relative abundance of neutrophils, the counting process is laborious and subjective. In this article, we propose the use of a custom trained “you only look once” (YOLO) machine learning algorithm to automate the identification and counting of fluorescently labeled neutrophils in zebrafish. Using this model, we found the correlation coefficient between human counting and the model equals r = 0.8207 with an 8.65% percent error, while variation among human counters was 5% to 12%. Importantly, the model was able to correctly validate results of a previously published article that quantitated neutrophils manually. While the accuracy can be further improved, this model notably achieves these results in mere minutes compared with hours via standard manual counting protocols and can be performed by anyone with basic programming knowledge. It further supports the use of deep learning models for high throughput analysis of fluorescently labeled blood cells in the zebrafish model system.
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影响因子：
3.1
作者：
Ishaq, Omer;Sadanandan, Sajith Kecheril;Wahlby, Carolina
通讯作者：
Wahlby, Carolina
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
DOI：
10.3791/61035
发表时间：
2021-04-29
期刊：
Journal of visualized experiments : JoVE
影响因子：
--
作者：
Rueb KF;Stachura DL
通讯作者：
Stachura DL
影响因子：
48
作者：
Falk, Thorsten;Mai, Dominic;Ronneberger, Olaf
通讯作者：
Ronneberger, Olaf
影响因子：
3.7
作者：
Berrun A;Harris E;Stachura DL
通讯作者：
Stachura DL