Bright field microscopic cells counting method for BEVS using nonlinear convergence index sliding band filter

BioMedical Engineering OnLine

Dong Sui³  Kuanquan Wang³  Heemin Park²  Jinseok Chae¹ 

[1] Department of Computer Science and Engineering, Incheon National University, Incheon, Korea

[2] Department of Computer Software Engineering, Sangmyung University, Cheonan, Korea

[3] Biocomputing Research Center, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

关键词: Microscopy image processing;    BEVS;    Transformed sliding band filter;    Cell counting;   

Others  :  1084251
DOI  :  10.1186/1475-925X-13-147

 received in 2014-06-14, accepted in 2014-09-02,  发布年份 2014

【 摘 要 】

Background

The Baculovirus Expression Vector System (BEVS) is a very popular expression vector system in gene engineering. An effective host cell line cultivation protocol can facilitate the baculovirus preparation and following experiments. However, the counting of the number of host cells in the protocol is usually performed by manual observation with microscopy, which is time consuming and labor intensive work, and prone to errors for one person or between different individuals. This study aims at giving a bright field insect cells counting protocol to help improve the efficient of BEVS.

Method

To develop a reliable and accurate counting method for the host cells in the bright field, such as Sf9 insect cells, a novel method based on a nonlinear Transformed Sliding Band Filter (TSBF) was proposed. And 3 collaborators counted cells at the same time to produce the ground truth for evaluation. The performance of TSBF method was evaluated with the image datasets of Sf9 insect cells according to the different periods of cell cultivation on the cell density, error rate and growth curve.

Results

The average error rate of our TSBF method is 2.21% on average, ranging from 0.89% to 3.97%, which exhibited an excellent performance with its high accuracy in lower error rate compared with traditional methods and manual counting. And the growth curve was much the manual method well.

Conclusion

Results suggest the proposed TSBF method can detect insect cells with low error rate, and it is suitable for the counting task in BEVS to take the place of manual counting by humans. Growth curve results can reflect the cells’ growth manner, which was generated by our proposed TSBF method in this paper can reflected the similar manner with it’s from the manual method. All of these proven that the proposed insect cell counting method can clearly improve the efficiency of BEVS.

【 授权许可】

   
2014 Sui et al.; licensee BioMed Central Ltd.

【 图 表 】

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