Automated Cell Counting using Image Processing
Keywords:Image Processing, Automated Cell Counting, CellProfiler, Synthetic Cell Images, Histology Cell Images
Manual cell counting using Hemocytometer is commonly used to quantify cells, as it is an inexpensive and versatile method. However, it is labour-intensive, tedious, and time-consuming. On the other hand, most automated cell counting methods are expensive and require experts to operate. Thus, the use of image analysis software allows one to access low-cost but robust automated cell counting. This study explores the advanced setting of image processing software to obtain routes with the highest counting accuracy. The results show the effectiveness of advanced settings in CellProfiler for counting cells from synthetic images. Two routes were found to give the highest performance, with average image and cell accuracies of 85% and 99.8%, respectively, and the highest F1 score of 0.83. However, the two routes were unable to correctly determine the exact number of cells on the histology images, albeit giving a respectable cell accuracy of 79.6%. Further investigation has shown that CellProfiler is able to correctly identify the bulk of the cells within the histology images. Good image quality with high focus and less blur was identified as the key to successful image-based cell counting. To further enhance the accuracy, other modules can be included to further segment an object hence improving the number of objects identified. Future work can focus on evaluating the robustness of the routes by comparing them with other methods and validating with the manual cell counting method.
M. C. Phelan and G. Lawler, “Cell counting,” Curr. Protoc. Cytom., vol. 00, no. 1, pp. 1–4, 1997. https://doi.org/10.1002/0471142956.cya03as00.
S. Nakada, J. Kohara, and K. Makita, “Estimation of circulating bovine leukemia virus levels using conventional blood cell counts,” J. Dairy Sci., vol. 101, no. 12, pp. 11229–11236, 2018. https://doi.org/10.3168/jds.2018-14609.
K. Ongena, C. Das, J. L. Smith, S. Gil, and G. Johnston, “Determining cell number during cell culture using the scepter cell counter,” J. Vis. Exp., no. 45, pp. 1–5, 2010. https://doi.org/10.3791/2204-v.
J. Lojk, L. Šajn, U. Čibej, and M. Pavlin, “Automatic cell counter for cell viability estimation,” Proceedings of the 2014 37th Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO'2014, no. May, pp. 239–244, 2014. https://doi.org/10.1109/MIPRO.2014.6859568.
R. Green and S. Wachsmann-Hogiu, “Development, history, and future of automated cell counters,” Clin. Lab. Med., vol. 35, no. 1, pp. 1–10, 2015. https://doi.org/10.1016/j.cll.2014.11.003.
J. B. Dixon, M. Faulkner, and J. R. Green, “Electronic counting of dog leucocytes. Discrepancies arising from calibration with Coulter standard 4C and with the haemocytometer,” Res. Vet. Sci., vol. 31, no. 2, pp. 249–252, 1981. https://doi.org/10.1016/S0034-5288(18)32503-7.
Z. Xu, “A high accurate static contact angle algorithm based on Hough transformation,” Measurement, vol. 55, pp. 97–100, 2014. https://doi.org/10.1016/j.measurement.2014.04.030.
P. Patil, “Counting of WBCS and RBCS from blood images using gray thresholding,” Int. J. Res. Eng. Technol., vol. 03, pp. 391–395, 2014. https://doi.org/10.15623/ijret.2014.0304071.
L. C. Muskat, Y. Kerkhoff, P. Humbert, T. W. Nattkemper, J. Eilenberg, and A. V Patel, “Image analysis-based quantification of fungal sporulation by automatic conidia counting and gray value correlation,” MethodsX, vol. 8, p. 101218, 2021. https://doi.org/10.1016/j.mex.2021.101218.
G. K. Chadha, A. Srivastava, A. Singh, R. Gupta, and D. Singla, “An automated method for counting red blood cells using image processing,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 769–778, 2020. https://doi.org/10.1016/j.procs.2020.03.408
Z. Zhang, Q. Li, W. Song, P. Wei, and J. Guo, “A novel concavity based method for automatic segmentation of touching cells in microfluidic chips,” Expert Syst. Appl., vol. 202, p. 117432, 2022. https://doi.org/10.1016/j.eswa.2022.117432.
D. S. Depto et al., “Automatic segmentation of blood cells from microscopic slides: A comparative analysis,” Tissue Cell, vol. 73, p. 101653, 2021. https://doi.org/10.1016/j.tice.2021.101653.
P. Maji, A. Mandal, M. Ganguly, and S. Saha, “An automated method for counting and characterizing red blood cells using mathematical morphology,” Proceedings of the 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), 2015, pp. 1–6. https://doi.org/10.1109/ICAPR.2015.7050674.
W. F. Martins, D. A. Longhi, G. M. F. de Aragão, B. Melero, J. Rovira, and A. M. Diez, “A mathematical modeling approach to the quantification of lactic acid bacteria in vacuum-packaged samples of cooked meat: Combining the TaqMan-based quantitative PCR method with the plate-count method,” Int. J. Food Microbiol., vol. 318, p. 108466, 2020. https://doi.org/10.1016/j.ijfoodmicro.2019.108466.
V. Ljosa, K. L. Sokolnicki, and A. E. Carpenter, “Annotated high-throughput microscopy image sets for validation,” Nat. Methods, vol. 9, no. 7, p. 637, 2012. https://doi.org/10.1038/nmeth.2083.
P. Naylor, M. Laé, F. Reyal, and T. Walter, “Segmentation of nuclei in histopathology images by deep regression of the distance map,” IEEE Trans. Med. Imaging, vol. 38, no. 2, pp. 448–459, 2019. https://doi.org/10.1109/TMI.2018.2865709.
A. E. Carpenter et al., “CellProfiler: Image analysis software for identifying and quantifying cell phenotypes,” Genome Biol., vol. 7, no. 10, 2006.
C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, “NIH Image to ImageJ: 25 years of image analysis,” Nat. Methods 2012 97, vol. 9, no. 7, pp. 671–675, 2012. https://doi.org/10.1038/nmeth.2089.
L. Sultana and A. G. Sanchis, “Establishing the lower bacterial concentration threshold for different optical counting techniques,” J. Microbiol. Methods, vol. 203, p. 106620, 2022. https://doi.org/10.1016/j.mimet.2022.106620.
L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 6, pp. 583–598, 1991. https://doi.org/10.1109/34.87344.
J. Raihan A, P. E. Abas, and L. C. De Silva, “Depth estimation for underwater images from single view image,” IET Image Process., vol. 14, no. 16, pp. 4188–4197, 2020. https://doi.org/10.1049/iet-ipr.2019.1533.
S. Aneja, N. Aneja, E. Abas, and A. G. Naim, “Transfer learning for cancer diagnosis in histopathological images,” IAES Int. J. Artif. Intell., vol. 11, no. 1, pp. 129–136, 2022. https://doi.org/10.11591/ijai.v11.i1.pp129-136.
R. Murugaiya, P. E. Abas, K. Mohanchandra, and D. S. Liyanage, “Robust cepstral feature for bird sound classification,” Int. J. Electr. Comput. Eng., vol. 12, pp. 1477–1487, 2022. https://doi.org/10.11591/ijece.v12i2.pp1477-1487.
M. G. M. Milani, P. E. Abas, and L. C. De Silva, “Identification of normal and abnormal heart sounds by prominent peak analysis,” Proceedings of the ACM International Conference Proceeding Series, 2019, pp. 31-35. https://doi.org/10.1145/3364908.3364924.
G. Cincotti, L. Nichele, M. Lucidi, and V. Persichetti, “Quantitative evaluation of ImageJ thresholding algorithms for microbial cell counting,” OSA Contin., vol. 3, no. 6, pp. 1417–1427, 2020. https://doi.org/10.1364/OSAC.393971.
T. Rasal, T. Veerakumar, B. N. Subudhi, and S. Esakkirajan, “Segmentation and counting of multiple myeloma cells using IEMD based deep neural network,” Leuk. Res., vol. 122, p. 106950, 2022. https://doi.org/10.1016/j.leukres.2022.106950.
A. Ossinger et al., “A rapid and accurate method to quantify neurite outgrowth from cell and tissue cultures: Two image analytic approaches using adaptive thresholds or machine learning,” J. Neurosci. Methods, vol. 331, p. 108522, 2020. https://doi.org/10.1016/j.jneumeth.2019.108522.
S. Wienert et al., “Detection and segmentation of cell nuclei in virtual microscopy images: A minimum-model approach,” Sci. Rep., vol. 2, pp. 1–7, 2012. https://doi.org/10.1038/srep00503.
A. LaTorre, L. Alonso-Nanclares, J. M. Peña, and J. DeFelipe, “3D segmentation of neuronal nuclei and cell-type identification using multi-channel information,” Expert Syst. Appl., vol. 183, p. 115443, 2021. https://doi.org/10.1016/j.eswa.2021.115443.
H. Zhang and J. Yao, “Automatic Focusing Method of Microscopes Based on Image Processing,” Math. Probl. Eng., vol. 2021, p. 8243072, 2021. https://doi.org/10.1155/2021/8243072.
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