Abstract
The cell counting process is an important procedure for various cell and cell-related research applications. Many life science-related studies examine the cells to compare results concerning cell numbers and variations. Most of the related studies are conducted using manual counting methods. However, manual counting is difficult, time-consuming, and fallible. This study proposes an automated cell counting software using computer vision (CV) technology and experimental investigation for automated cell and bacterium counting. The software processes images for calculating cell/bacterium count, concerning pre-defined user parameters. In the experiments, cell and bacteria calculations are tested for single and mixed variations. Experimental results are examined by comparing manual and automated cell counting results. The accuracy of the software is found for calculating the cell count of a single and mixed cell/bacteria solution to be 99% and 98%, respectively. Also, the software can process video and camera streams in real-time in the same manner. The proposed open-sourced CV software can be used in biomedical and fundamental biological research studies for rapidly determining target cell numbers.
Supporting Institution
Aydın Adnan Menderes University Research Fund
Project Number
MF-20002
Thanks
The authors thank Mustafa Duran for fruitful discussions of the experimental design. This research was supported by Aydın Adnan Menderes University Research Fund. Project Number: MF-20002.
References
- Ongena K, Das C, Smith JL, Gil S, Johnston G. Determining cell number during cell culture using the Scepter cell counter. J. Vis. Exp. 2010;45:2204.
- O’Brien J, Hayder H, Peng C. Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins. J. Vis. Exp. 2016;117:54719.
- Brown MR, Hands CL, Coello-Garcia T, Sani BS, Ott AIG, Smith SJ, et al. A flow cytometry method for bacterial quantification and biomass estimates in activated sludge. J. Microbiol. Methods. 2019;160:73–83.
- Ricchi M, Bertasio C, Boniotti MB, Vicari N, Russo S, Tilola M, et al. Comparison among the Quantification of Bacterial Pathogens by qPCR, dPCR, and Cultural Methods. Front. Microbiol. 2017;8.
- Cadena-Herrera D, Esparza-De Lara JE, Ramírez-Ibañez ND, López-Morales CA, Pérez NO, Flores-Ortiz LF, et al. Validation of three viable-cell counting methods: Manual, semi-automated, and automated. Biotechnol. Reports. 2015;7:9-16.
- Piccinini F, Tesei A, Arienti C, Bevilacqua A. Cell Counting and Viability Assessment of 2D and 3D Cell Cultures: Expected Reliability of the Trypan Blue Assay. Biol. Proced. Online. 2017;19.
- Freund M, Carol B. Factors Affecting Haemocytometer Counts Of Sperm Concentration In Human Semen. Reproduction. 1964;8:149–155.
- Green R, Wachsmann-Hogiu S. Development, History, and Future of Automated Cell Counters. Clin. Lab. Med. 2015;35:1–10.
- McKinnon KM. Flow Cytometry: An Overview. Curr. Protoc. Immunol. 2018;120.
- Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods. 2012;9(7):671–5.
- Onat C, Daskin M, Toraman S, Golgiyaz S, Talu MF. Prediction of combustion states from flame image in a domestic coal burner. Meas. Sci. Technol. 2021.
- Golgiyaz S, Talu MF, Onat C. Görüntü İşleme ve Makine Öğrenmesi Yöntemleri ile Baca Gazı Sıcaklığının Tahmin Edilmesi. Eur. J. Sci. Technol. 2019;16:283–291.
- Akkoyun F, Ozcelik A. A Simple Approach for Controlling an Open-Source Syringe Pump. Eur. Mech. Sci. 2020;4(4):166–170.
Abstract
Hücre sayım işlemi, çeşitli hücre ve hücreler ile ilişkili araştırma uygulamalarında kullanılan önemli bir prosedürdür. Fen bilimleri alanında çoğu araştırmada hücreler incelenirken, hücre sayısı ve ilgili hesaplamalar ile sonuçların karşılaştırılması yapılmaktadır. Bu alandaki çalışmalarda yaygın olarak manuel olarak sayım yöntemi kullanılmaktadır. Ancak manuel sayım zaman alıcı, zorlu ve hataya meyilli bir ölçüm yöntemidir. Bu çalışmada, bilgisayarlı görüş (CV) teknolojisi kullanan otomatik hücre sayım yazılımı sunulmaktadır. Hücre ile bakteri örneklerinin otomatik sayımı deneysel çalışma yapılarak test edilmiş ve deneysel sonuçlar, manuel ve otomatik hücre sayımı yöntemlerinden elde edilen sonuçların karşılaştırılmasıyla incelenmiştir. Geliştirilmiş olan yazılım önceden tanımlanmış kullanıcı değişkenleri doğrultusunda, görüntüleri hücre/bakteri sayısını hesaplamak için incelemektedir. Yazılımın, tek türde hücre/bakteri için %99 ve karışık hücre/bakterilerde %98 sayım doğruluğuna ulaştığı görülmüştür. Buna ek olarak, aynı yazılım ile, video ve gerçek zamanlı kamera görüntüleri de aynı amaçla işlenebilmektedir. Bu çalışmada önerilmiş olan açık kaynak kodlu CV yazılımı, birçok araştırmacı tarafından, bir çok araştırmada, otomatik olarak hücre sayımının yapılmasında kullanılabilecektir.
Project Number
MF-20002
References
- Ongena K, Das C, Smith JL, Gil S, Johnston G. Determining cell number during cell culture using the Scepter cell counter. J. Vis. Exp. 2010;45:2204.
- O’Brien J, Hayder H, Peng C. Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins. J. Vis. Exp. 2016;117:54719.
- Brown MR, Hands CL, Coello-Garcia T, Sani BS, Ott AIG, Smith SJ, et al. A flow cytometry method for bacterial quantification and biomass estimates in activated sludge. J. Microbiol. Methods. 2019;160:73–83.
- Ricchi M, Bertasio C, Boniotti MB, Vicari N, Russo S, Tilola M, et al. Comparison among the Quantification of Bacterial Pathogens by qPCR, dPCR, and Cultural Methods. Front. Microbiol. 2017;8.
- Cadena-Herrera D, Esparza-De Lara JE, Ramírez-Ibañez ND, López-Morales CA, Pérez NO, Flores-Ortiz LF, et al. Validation of three viable-cell counting methods: Manual, semi-automated, and automated. Biotechnol. Reports. 2015;7:9-16.
- Piccinini F, Tesei A, Arienti C, Bevilacqua A. Cell Counting and Viability Assessment of 2D and 3D Cell Cultures: Expected Reliability of the Trypan Blue Assay. Biol. Proced. Online. 2017;19.
- Freund M, Carol B. Factors Affecting Haemocytometer Counts Of Sperm Concentration In Human Semen. Reproduction. 1964;8:149–155.
- Green R, Wachsmann-Hogiu S. Development, History, and Future of Automated Cell Counters. Clin. Lab. Med. 2015;35:1–10.
- McKinnon KM. Flow Cytometry: An Overview. Curr. Protoc. Immunol. 2018;120.
- Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods. 2012;9(7):671–5.
- Onat C, Daskin M, Toraman S, Golgiyaz S, Talu MF. Prediction of combustion states from flame image in a domestic coal burner. Meas. Sci. Technol. 2021.
- Golgiyaz S, Talu MF, Onat C. Görüntü İşleme ve Makine Öğrenmesi Yöntemleri ile Baca Gazı Sıcaklığının Tahmin Edilmesi. Eur. J. Sci. Technol. 2019;16:283–291.
- Akkoyun F, Ozcelik A. A Simple Approach for Controlling an Open-Source Syringe Pump. Eur. Mech. Sci. 2020;4(4):166–170.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Project Number | MF-20002 |
| Publication Date | June 25, 2021 |
| Published in Issue | Year 2021 Volume: 10 Issue: 1 |
Cite
Cited By
Inexpensive multispectral imaging device
Instrumentation Science & Technology
https://doi.org/10.1080/10739149.2022.2047061
Automated Grain Counting for the Microstructure of Mg Alloys Using an Image Processing Method
Journal of Materials Engineering and Performance
https://doi.org/10.1007/s11665-021-06436-2
This work is licensed under the Creative Commons Attribution-Non-Commercial-Non-Derivable 4.0 International License.