Automated Cell Viability Analysis in Tissue Scaffolds

Year 2023, Volume: 51 Issue: 1, 37 - 50, 01.01.2023

Tansel Uyar Aykut Erdamar Menemşe Gümüşderelioğlu Mehmet Feyzi Akşahin Gülseren Irmak Osman Eroğul

https://doi.org/10.15671/hjbc.868396

Abstract

Image processing techniques are frequently used for extracting quantitative information (cell area, cell size, cell counting, etc.) from different types of microscopic images. Image analysis of cell biology and tissue engineering is time consuming and requires personal expertise. In addition, evaluation of the results may be subjective. Therefore, computer-based learning applications have been rapidly developed in recent years. In this study, Confocal Laser Scanning Microscope (CLSM) images of the viable pre-osteoblastic mouse MC3T3-E1 cells in 3D bioprinted tissue scaffolds, captured from a bone tissue regeneration study, were analyzed by using image processing techniques. The goal of this study is to develop a reliable and fast algorithm for semi-automatic analysis of CLSM images. Percentages of live and dead cell areas in the scaffolds were determined with image correlation, and then, total cell viabilities were calculated. The other goal of this study is to determine the depth profile of viable cells in 3D tissue scaffold. Manual measurements of four different analysts were obtained. The measurement variations of analysts, also known as the coefficient of variation, were determined from 13.18% to 98.34% for live cell images and from 9.75% to 126.02% for dead cell images. To overcome this subjectivity, a semi-automatic algorithm was developed. Consequently, cross-sectional image sets of three different types of tissue scaffolds were analyzed. As a result, maximum cell viabilities were obtained at intervals of 63 µm and 90 µm from the scaffold surface.

Keywords

image correlation, edge detection, cell area counting, tissue engineering

References

• [1] V. Ntziachristos, Fluorescence Molecular Imaging, Annu. Rev. Biomed. Eng., 8 (2006) 1–33.
• [2] G.Y. Wiederschain, The Molecular Probes handbook A guide to fluorescent probes and labeling technologies, 11th ed., Thermo Fisher Scientific, 2010.
• [3] W. Grootjans, E.A. Usmanij, W.J.G. Oyen, E.H.F.M. van der Heijden, E.P. Visser, D. Visvikis, M. Hatt, J. Bussink, L.F. de Geus-Oei, Performance of automatic image segmentation algorithms for calculating total lesion glycolysis for early response monitoring in non-small cell lung cancer patients during concomitant chemoradiotherapyFDG-PET in early NSCLC response assessment, Radiother. Oncol., 119 (2016) 473–479.
• [4] Y. Liu, Y. Chen, B. Han, Y. Zhang, X. Zhang, Y. Su, Fully automatic Breast ultrasound image segmentation based on fuzzy cellular automata framework, Biomed. Signal Process. Control, 40 (2018) 433–442.
• [5] X. Li, J. Liu, Z. Liu, X. He, C. Zhang, H. Yuan, F. Liu, C. Zheng, Automatic detection of leukocytes for cytometry with color decomposition, Optik (Stuttg)., 127 (2016) 11901–11910.
• [6] G. Narayanan, M.Y. Tekbudak, Y. Caydamli, J. Dong, W.E. Krause, Accuracy of electrospun fiber diameters: The importance of sampling and person-to-person variation, Polym. Test., 61 (2017) 240–248.
• [7] S. Nazlibilek, D. Karacor, T. Ercan, M.H. Sazli, O. Kalender, Y. Ege, Automatic segmentation, counting, size determination and classification of white blood cells, Meas. J. Int. Meas. Confed., 55 (2014) 58–65.
• [8] J. Malašauskiene, R. Milašius, Investigation and estimation of structure of web from electrospun nanofibres, J. Nanomater., 2013 (2013).
• [9] F. Brun, G. Turco, A. Accardo, S. Paoletti, Automated quantitative characterization of alginate/hydroxyapatite bone tissue engineering scaffolds by means of micro-CT image analysis, J. Mater. Sci. Mater. Med., 22 (2011) 2617–2629.
• [10] H. Ramoser, V. Laurain, H. Bischof, R. Ecker, Leukocyte segmentation and classification in blood-smear images, Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 4 (2005) 3371–3374.
• [11] N. Guo, L. Zeng, Q. Wu, A method based on multispectral imaging technique for White Blood Cell segmentation, Comput. Biol. Med., 37 (2007) 70–76.
• [12] C. Di Ruberto, A. Dempster, S. Khan, B. Jarra, Analysis of infected blood cell images using morphological operators, Image Vis. Comput., 20 (2002) 133–146.
• [13] Q. Liao, Y. Deng, An accurate segmentation method for white blood cell images, Proc. - Int. Symp. Biomed. Imaging, (2002) 245–248.
• [14] S.H. Rezatofighi, H. Soltanian-Zadeh, Automatic recognition of five types of white blood cells in peripheral blood, Comput. Med. Imaging Graph., 35 (2011) 333–343.
• [15] Ö. Kasım, A.E. Kuzucuoğlu, Lökosit hücrelerinin preparat görüntüsünden tespiti ve sınıflandırılması, Gazi Üniv. Müh. Mim. Fak. Der., 30 (2015) 95–109.
• [16] N. Dehghan, M.A. Tavanaie, P. Payvandy, Morphology study of nanofibers produced by extraction from polymer blend fibers using image processing, Korean J. Chem. Eng., 32 (2015) 1928–1937.
• [17] E.S. Gelsema, Application of the Method of Multiple Thresholding to White Blood Cell Classification, Comput. Biol. Med., 18 (1988) 65–74.
• [18] L. Zhao, K. Li, M. Wang, J. Yin, E. Zhu, C. Wu, S. Wang, C. Zhu, Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF, Comput. Biol. Med., 71 (2016) 46–56.
• [19] X. Bai, P. Wang, C. Sun, Y. Zhang, F. Zhou, C. Meng, Finding splitting lines for touching cell nuclei with a shortest path algorithm, Comput. Biol. Med., 63 (2015) 277–286.
• [20] D. Yu, T.D. Pham, X. Zhou, Analysis and recognition of touching cell images based on morphological structures, Comput. Biol. Med., 39 (2009) 27–39.
• [21] J.J. Stanger, N. Tucker, N. Buunk, Y.B. Truong, A comparison of automated and manual techniques for measurement of electrospun fibre diameter, Polym. Test., 40 (2014) 4–12.
• [22] H. Shen, A.S. Goldstein, G. Wang, Biomedical Imaging and Image Processing in Tissue Engineering, in: N. Pallua, C. V. Suschek (Eds.), Tissue Eng. From Lab to Clin., 1st ed., Springer-Verlag Berlin Heidelberg, New York, 2011: pp. 155–178.
• [23] P.M. Kulkarni, E. Barton, M. Savelonas, R. Padmanabhan, Y. Lu, K. Trett, W. Shain, J.L. Leasure, B. Roysam, Quantitative 3-D analysis of GFAP labeled astrocytes from fluorescence confocal images, J. Neurosci. Methods, 246 (2015) 38–51.
• [24] G. Lin, U. Adiga, K. Olson, J.F. Guzowski, C.A. Barnes, B. Roysam, A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks, Cytometry, 56A (2003) 23–36.
• [25] F. Piccinini, A. Tesei, G. Paganelli, W. Zoli, A. Bevilacqua, Improving reliability of live/dead cell counting through automated image mosaicing, Comput. Methods Programs Biomed., 117 (2014) 448–463.
• [26] H.L. More, J. Chen, E. Gibson, J.M. Donelan, M.F. Beg, A semi-automated method for identifying and measuring myelinated nerve fibers in scanning electron microscope images, J. Neurosci. Methods, 201 (2011) 149–158.
• [27] T.T. Demirtaş, G. Kaynak, M. Gumuşderelioʇlu, Bone-like hydroxyapatite precipitated from 10×SBF-like solution by microwave irradiation, Mater. Sci. Eng. C, 49 (2015) 713–719.
• [28] A.I. Van Den Bulcke, B. Bogdanov, N. De Rooze, E.H. Schacht, M. Cornelissen, H. Berghmans, Structural and rheological properties of methacrylamide modified gelatin hydrogels, Biomacromolecules, 1 (2000) 31–38.
• [29] G. Irmak, T.T. Demirtaş, M. Gümüşderelioǧlu, Highly Methacrylated Gelatin Bioink for Bone Tissue Engineering, ACS Biomater. Sci. Eng., 5 (2019) 831–845.
• [30] F. Jin, P. Fieguth, L. Winger, E. Jernigan, Adaptive Wiener filtering of noisy images and image sequences, in: Proc. 2003 Int. Conf. Image Process. (Cat. No.03CH37429), IEEE, 2003: pp. III-349–52.
• [31] P. Shukla, A. Boyat, Image Denoising using Local Adaptive Wiener Filter in Spatial and Temporal Domain, Int. J. Adv. Res. Electron. Commun. Eng., 4 (2015) 2019–2024.
• [32] J. Sen Lee, Digital Image Enhancement and Noise Filtering by Use of Local Statistics, IEEE Trans. Pattern Anal. Mach. Intell., PAMI-2 (1980) 165–168.
• [33] J.S. Lim, Two-Dimensional Signal and Image Processing, 1st ed., Printice Hall, Englewood Cliffs, New Jersey, 1990.
• [34] J.S. Lee, Refined filtering of image noise using local statistic, Comput. Graph. Image Process., 24 (1983) 255–269.
• [35] M.S. Nixon, A.S. Aguado, Feature Extraction & Image Processing for Computer Vision, 3rd ed., Elsevier Inc., London, 2012.
• [36] M.P. Wand, C.M. Jones, Kernel Smoothing, First Edit, Chapman & Hall, New York, 1995.
• [37] R. Gonzalez, R. Woods, B. Masters, Digital Image Processing, Third Edition, Third Edit, Upper Saddle River, New Jersey, 2007.
• [38] M. Bizrah, S.C. Dakin, L. Guo, F. Rahman, M. Parnell, E. Normando, S. Nizari, B. Davis, A. Younis, M.F. Cordeiro, A semi-automated technique for labeling and counting of apoptosing retinal cells, BMC Bioinformatics, 15 (2014) 169.