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Automated Confluence Measurement Method for Mesenchymal Stem Cell from Brightfield Microscopic Images

Published online by Cambridge University Press:  03 September 2021

Zenan Wang ,
Rucai Zhan  and
Ying Hu
Zenan Wang*
Affiliation:
Chinese Academy of Science, Shenzhen Institute of Advanced Technology, 1068 Xueyuan Avenue, Shenzhen 518000, China
Rucai Zhan
Affiliation:
Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, 16766 JingShi Road, Jinan 250014, China
Ying Hu
Affiliation:
Chinese Academy of Science, Shenzhen Institute of Advanced Technology, 1068 Xueyuan Avenue, Shenzhen 518000, China
*
*Corresponding author: Zenan Wang, E-mail: [email protected]
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Abstract

Cell confluence is an important metric in cell culture, as proper timing is essential to maintain cell phenotype and culture quality. To estimate cell confluence, transparent cells are observed under a phase-contrast or differential interference contrast microscope by a biologist, whose estimations are error-prone and subjective. To overcome the necessity of using the phase-contrast microscope and reducing intra- and inter-observer errors, we have proposed an algorithm that automatically measures cell confluence by using a commonly used brightfield microscope. The proposed method consists of a transport-of-intensity equation-based brightfield microscopic image processing, an image reconstruction method, and an adaptive image segmentation method based on edge detection, entropy filtering, and range filtering. Experimental results have shown that our method has outperformed several popular algorithms, with an F-score of 0.84 ± 0.07, in images with various cell confluence values. The proposed algorithm is robust and accurate enough to perform confluence measurement with various lighting conditions under a low-cost brightfield microscope, making it simple and cost-effective to use for a fully automated cell culture process.

Keywords

brightfield microscopic imagingcell confluencecell image processingcell recognitiontransport of intensity equation
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 27 , Issue 5 , October 2021 , pp. 1093 - 1101
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

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References

Afridi, MJ, Chun, L, Chan, C, Baek, S & Liu, X (2014). Image segmentation of mesenchymal stem cells in diverse culturing conditions. In IEEE Winter Conference on Applications of Computer Vision, pp. 516–523.CrossRefGoogle Scholar
Allman, B, Nassis, L, Von Bibra, M, Bellair, C, Kabbara, A, Barone-Nugent, E, Gaeth, A, Delbridge, L & Nugent, K (2002). Optical phase microscopy: Quantitative imaging and conventional phase analogs. Microsc Anal 52, 13–15.Google Scholar
Ambühl, ME, Brepsant, C, Meister, JJ, Verkhovsky, AB & Sbalzarini, IF (2012). High-resolution cell outline segmentation and tracking from phase-contrast microscopy images. J Microsc 245, 161–170.CrossRefGoogle ScholarPubMed
Awad, SI, Abdallat, R, Smadi, O & Al-Momani, TD (2019). Automated identification and counting of proliferating mesenchymal stem cells in bone callus. Int J Comput Vis Robotics 9, 1–13.CrossRefGoogle Scholar
Barty, A, Nugent, KA, Paganin, D & Roberts, A (1998). Quantitative optical phase microscopy. Opt Lett 23, 817–819.CrossRefGoogle ScholarPubMed
Bian, Z, Guo, C, Jiang, S, Zhu, J, Wang, R, Song, P, Zhang, Z, Hoshino, K & Zheng, G (2020). Autofocusing technologies for whole slide imaging and automated microscopy. J Biophotonics 13, e202000227.CrossRefGoogle ScholarPubMed
Bostan, E, Froustey, E, Nilchian, M, Sage, D & Unser, M (2016). Variational phase imaging using the transport-of-intensity equation. IEEE Trans Image Process 25, 807–817.CrossRefGoogle ScholarPubMed
Bradhurst, CJ, Boles, W & Xiao, Y (2008). Segmentation of bone marrow stromal cells in phase contrast microscopy images. In 2008 23rd International Conference Image and Vision Computing New Zealand, pp. 1–6.CrossRefGoogle Scholar
Busschots, S, O'Toole, S, O'Leary, JJ & Stordal, B (2015). Non-invasive and non-destructive measurements of confluence in cultured adherent cell lines. MethodsX 2, 8–13.CrossRefGoogle ScholarPubMed
Chalfoun, J, Majurski, M, Peskin, A, Breen, C, Bajcsy, P & Brady, M (2015). Empirical gradient threshold technique for automated segmentation across image modalities and cell lines. J Microsc 260, 86–99.CrossRefGoogle ScholarPubMed
Chiu, C-H, Leu, J-D, Lin, T-T, Su, P-H, Li, W-C, Lee, Y-J & Cheng, D-C (2020). Systematic quantification of cell confluence in human normal oral fibroblasts. Appl Sci 10, 9146.CrossRefGoogle Scholar
Chourasiya, S & Rani, GU (2014). A novel automatic red blood cell counting system using fuzzy C-means clustering.Google Scholar
Davis, QT, Tanaka, T & McGloin, D (2017). Transport of intensity microscopy for distinguishing single and bundled microtubules. In 2017 European Conference on Lasers and Electro-Optics and European Quantum Electronics Conference, p. JSII_P_5. Munich: Optical Society of America.Google Scholar
Dempsey, KP, Richardson, JB & Lam, KP (2014). On measuring cell confluence in phase contrast microscopy. In Proc.SPIE, Vol. 8947.Google Scholar
Gao, S, McGarry, M, Ferrier, T, Pallante, B, Priddle, H, Gasparrini, B, Fletcher, J, Harkness, L, De Sousa, P, McWhir, J & Wilmut, I (2003). Effect of cell confluence on production of cloned mice using an inbred embryonic stem cell line. Biol Reprod 68, 595–603.CrossRefGoogle ScholarPubMed
Ginty, PJ, Howard, D, Rose, FR, Whitaker, MJ, Barry, JJ, Tighe, P, Mutch, SR, Serhatkulu, G, Oreffo, RO, Howdle, SM & Shakesheff, KM (2006). Mammalian cell survival and processing in supercritical CO(2). Proc Natl Acad Sci USA 103, 7426–7431.CrossRefGoogle Scholar
Grant, SD, Richford, K, Burdett, HL, McKee, D & Patton, BR (2020). Low-cost, open-access quantitative phase imaging of algal cells using the transport of intensity equation. R Soc Open Sci 7, 191921.CrossRefGoogle ScholarPubMed
Gureyev, TE & Nugent, KA (1997). Rapid quantitative phase imaging using the transport of intensity equation. Opt Commun 133, 339–346.CrossRefGoogle Scholar
Hamad, AH, Muhamad, HO & Yaba, S (2015). De-noising of medical images by using some filters.Google Scholar
Jaccard, N, Griffin, LD, Keser, A, Macown, RJ, Super, A, Veraitch, FS & Szita, N (2014). Automated method for the rapid and precise estimation of adherent cell culture characteristics from phase contrast microscopy images. Biotechnol Bioeng 111, 504–517.CrossRefGoogle ScholarPubMed
Juneau, PM, Garnier, A & Duchesne, C (2013). Selection and tuning of a fast and simple phase-contrast microscopy image segmentation algorithm for measuring myoblast growth kinetics in an automated manner. Microsc Microanal 19, 855–866.CrossRefGoogle Scholar
Katiyar, S & Pattathal, A (2012). Comparative analysis of common edge detection techniques in context of object extraction. IEEE Trans Geosci Remote Sens 50, 68–78.Google Scholar
Kato, R, Iejima, D, Agata, H, Asahina, I, Okada, K, Ueda, M, Honda, H & Kagami, H (2010). A compact, automated cell culture system for clinical scale cell expansion from primary tissues. Tissue Eng Part C Methods 16, 947–956.CrossRefGoogle ScholarPubMed
Kepp, O, Galluzzi, L, Lipinski, M, Yuan, J & Kroemer, G (2011). Cell death assays for drug discovery. Nat Rev Drug Discovery 10, 221–237.CrossRefGoogle ScholarPubMed
Ker, DF, Weiss, LE, Junkers, SN, Chen, M, Yin, Z, Sandbothe, MF, Huh, SI, Eom, S, Bise, R, Osuna-Highley, E, Kanade, T & Campbell, PG (2011). An engineered approach to stem cell culture: Automating the decision process for real-time adaptive subculture of stem cells. PLoS One 6, e27672.CrossRefGoogle ScholarPubMed
Lam, KP, Dempsey, KP, Collins, DJ & Richardson, JB (2016). Monitoring stem cells in phase contrast imaging. In Proc.SPIE, Vol. 9711.Google Scholar
Lim, K, Park, S, Kim, J, Seonwoo, H, Choung, P & Chung, J (2013). Cell image processing methods for automatic cell pattern recognition and morphological analysis of mesenchymal stem cells—An algorithm for cell classification and adaptive brightness correction. J Biosyst Eng 38, 55–63.CrossRefGoogle Scholar
Lm, LL, Rosas-Durazo, A, Jl, R-P, Acosta-Silva, A, Gil-Salido, AA, Eg, R-C & Sb, IA (2013). Cell growth curves for different cell lines and their relationship with biological activities.Google Scholar
Otsu, N (1979). A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9, 62–66.CrossRefGoogle Scholar
Ruutu, M, Johansson, B, Grenman, R, Syrjänen, K & Syrjänen, S (2004). Effect of confluence state and passaging on global cancer gene expression pattern in oral carcinoma cell lines. Anticancer Res 24, 2627–2631.Google ScholarPubMed
Secunda, R, Vennila, R, Mohanashankar, AM, Rajasundari, M, Jeswanth, S & Surendran, R (2015). Isolation, expansion and characterisation of mesenchymal stem cells from human bone marrow, adipose tissue, umbilical cord blood and matrix: A comparative study. Cytotechnology 67, 793–807.CrossRefGoogle ScholarPubMed
Seiler, C, Gazdhar, A, Reyes, M, Benneker, LM, Geiser, T, Siebenrock, KA & Gantenbein-Ritter, B (2014). Time-lapse microscopy and classification of 2D human mesenchymal stem cells based on cell shape picks up myogenic from osteogenic and adipogenic differentiation. J Tissue Eng Regen Med 8, 737–746.CrossRefGoogle ScholarPubMed
Soleimani, S, Mirzaei, M & Toncu, D-C (2017). A new method of SC image processing for confluence estimation. Micron 101, 206–212.CrossRefGoogle ScholarPubMed
Teague, MR (1983). Deterministic phase retrieval: A Green's function solution. J Opt Soc Am 73, 1434–1441.CrossRefGoogle Scholar
Theriault, DH, Walker, ML, Wong, JY & Betke, M (2012). Cell morphology classification and clutter mitigation in phase-contrast microscopy images using machine learning. Mach Vis Appl 23, 659–673.CrossRefGoogle Scholar
Topman, G, Sharabani-Yosef, O & Gefen, A (2011). A method for quick, low-cost automated confluency measurements. Microsc Microanal 17, 915–922.CrossRefGoogle ScholarPubMed
Vicar, T, Balvan, J, Jaros, J, Jug, F, Kolar, R, Masarik, M & Gumulec, J (2019). Cell segmentation methods for label-free contrast microscopy: Review and comprehensive comparison. BMC Bioinformatics 20, 360.CrossRefGoogle ScholarPubMed
Yin, Z, Li, K, Kanade, T & Chen, M (2010). Understanding the optics to aid microscopy image segmentation. Med Image Comput Comput Assist Interv 13, 209–217.Google ScholarPubMed
Zuo, C, Li, J, Sun, J, Fan, Y, Zhang, J, Lu, L, Zhang, R, Wang, B, Huang, L & Chen, Q (2020). Transport of intensity equation: A tutorial. Opt Lasers Eng 135, 106187.CrossRefGoogle Scholar