Yıl: 2023 Cilt: 51 Sayı: 1 Sayfa Aralığı: 37 - 50 Metin Dili: İngilizce DOI: 10.15671/hjbc.868396 İndeks Tarihi: 17-10-2023

Automated Cell Viability Analysis in Tissue Scaffolds

Öz:
Image analysis of cell biology and tissue engineering is time-consuming and requires personal expertise. However, evalu - ation of the results may be subjective. Therefore, computer-based learning and detection 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 aim of this study is to develop a reliable and fast algorithm for the automated analysis of live/dead assay CLSM images. Percentages of live and dead cell areas in the scaffolds were determined, and then, total cell viabilities were calculated. Furthermore, manual measurements of four different analysts were obtained to evaluate subjectivity in the analysis. 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. Therefore, an automated algorithm was developed to overcome this subjectivity. The other aim of this study is to determine the depth profile of viable cells in 3D tissue scaffolds. Consequently, cross-sectional image sets of three different types of tissue scaf - folds were analyzed.
Anahtar Kelime: image correlation edge detection cell area counting tissue engineering

Doku İskelelerinde Otomatik Hücre Canlılığı Analizi

Öz:
Hücre biyolojisi ve doku mühendisliğinde görüntü analizi zaman alan ve kişisel uzmanlık gerektiren bir işlemdir. Ancak so - nuçların değerlendirilmesi öznel olabilir. Bu nedenle bilgisayar destekli öğrenme ve tespit uygulamaları son yıllarda hızla gelişmiştir. Bu çalışmada, bir kemik dokusu rejenerasyon çalışmasından yakalanan, 3D biyo-baskılı doku iskelelerinde canlı pre-osteoblastik fare MC3T3-E1 hücrelerinin Konfokal Lazer Taramalı Mikroskop (CLSM) görüntüleri, görüntü işleme teknik - leri kullanılarak analiz edilmiştir. Bu çalışmanın amacı, canlı/ölü analizi CLSM görüntülerinin otomatik analizi için güvenilir ve hızlı bir algoritma geliştirmektir. İskelelerdeki canlı ve ölü hücre alanlarının yüzdeleri belirlenmiş ve ardından toplam hücre canlılıkları hesaplanmıştır. Ayrıca, analizde öznelliği değerlendirmek için dört farklı analistin manuel ölçümleri yapılmıştır. Varyasyon katsayısı olarak da bilinen analistlerin ölçüm varyasyonları, canlı hücre görüntüleri için % 13.18 ile % 98.34 ve ölü hücre görüntüleri için % 9.75 ile % 126.02 arasında belirlenmiştir. Bu nedenle, bu öznelliği aşmak için otomatik bir algoritma geliştirilmiştir. Bu çalışmanın diğer amacı, 3 boyutlu doku iskelelerindeki canlı hücrelerin derinlik profilini belirlemektir. So - nuç olarak, üç farklı doku iskelesinin kesitsel görüntü setleri analiz edilmiştir.
Anahtar Kelime: Görüntü korelasyonu Kenar algılama hücre alanı sayımı doku mühendisliği

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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  • 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.
  • 39. A.M. Abd El-Aziz, A. El-Maghraby, A. Ewald, S.H. Kandil, In- vitro cytotoxicity study: cell viability and cellmorphology of carbon nanofibrous scaffold/hydroxyapatite nanocomposites, Molecules, 26 (2021) 1552.
  • 40. R. Dittmar, E. Potier, M.v. Zandvoor, K. Ito, Assessment of Cell Viability in Three-Dimensional Scaffolds Using Cellular Auto-Fluorescence, Tissue Engineering: Part C, 18 (2012) 198-204.
  • 41. B. Gantenbein, A.S. Croft, M. Larraillet, Mammalian Cell Viability Methods in 3D Scaffolds for Tissue Engineering, in: N. Grigoryeva (Eds.), Fluorescence Methods for Investigation of Living Cells and Microorganisms, 1st ed., Intech Open, London, 2020: pp. 1-25.
APA Uyar T, Erdamar A, Gümüşderelioğlu M, Aksahin M, Family Name Deactivated G, EROGUL O (2023). Automated Cell Viability Analysis in Tissue Scaffolds. , 37 - 50. 10.15671/hjbc.868396
Chicago Uyar Tansel,Erdamar Aykut,Gümüşderelioğlu Menemşe,Aksahin Mehmet,Family Name Deactivated Given Names Deactivated,EROGUL Osman Automated Cell Viability Analysis in Tissue Scaffolds. (2023): 37 - 50. 10.15671/hjbc.868396
MLA Uyar Tansel,Erdamar Aykut,Gümüşderelioğlu Menemşe,Aksahin Mehmet,Family Name Deactivated Given Names Deactivated,EROGUL Osman Automated Cell Viability Analysis in Tissue Scaffolds. , 2023, ss.37 - 50. 10.15671/hjbc.868396
AMA Uyar T,Erdamar A,Gümüşderelioğlu M,Aksahin M,Family Name Deactivated G,EROGUL O Automated Cell Viability Analysis in Tissue Scaffolds. . 2023; 37 - 50. 10.15671/hjbc.868396
Vancouver Uyar T,Erdamar A,Gümüşderelioğlu M,Aksahin M,Family Name Deactivated G,EROGUL O Automated Cell Viability Analysis in Tissue Scaffolds. . 2023; 37 - 50. 10.15671/hjbc.868396
IEEE Uyar T,Erdamar A,Gümüşderelioğlu M,Aksahin M,Family Name Deactivated G,EROGUL O "Automated Cell Viability Analysis in Tissue Scaffolds." , ss.37 - 50, 2023. 10.15671/hjbc.868396
ISNAD Uyar, Tansel vd. "Automated Cell Viability Analysis in Tissue Scaffolds". (2023), 37-50. https://doi.org/10.15671/hjbc.868396
APA Uyar T, Erdamar A, Gümüşderelioğlu M, Aksahin M, Family Name Deactivated G, EROGUL O (2023). Automated Cell Viability Analysis in Tissue Scaffolds. Hacettepe Journal of Biology and Chemistry, 51(1), 37 - 50. 10.15671/hjbc.868396
Chicago Uyar Tansel,Erdamar Aykut,Gümüşderelioğlu Menemşe,Aksahin Mehmet,Family Name Deactivated Given Names Deactivated,EROGUL Osman Automated Cell Viability Analysis in Tissue Scaffolds. Hacettepe Journal of Biology and Chemistry 51, no.1 (2023): 37 - 50. 10.15671/hjbc.868396
MLA Uyar Tansel,Erdamar Aykut,Gümüşderelioğlu Menemşe,Aksahin Mehmet,Family Name Deactivated Given Names Deactivated,EROGUL Osman Automated Cell Viability Analysis in Tissue Scaffolds. Hacettepe Journal of Biology and Chemistry, vol.51, no.1, 2023, ss.37 - 50. 10.15671/hjbc.868396
AMA Uyar T,Erdamar A,Gümüşderelioğlu M,Aksahin M,Family Name Deactivated G,EROGUL O Automated Cell Viability Analysis in Tissue Scaffolds. Hacettepe Journal of Biology and Chemistry. 2023; 51(1): 37 - 50. 10.15671/hjbc.868396
Vancouver Uyar T,Erdamar A,Gümüşderelioğlu M,Aksahin M,Family Name Deactivated G,EROGUL O Automated Cell Viability Analysis in Tissue Scaffolds. Hacettepe Journal of Biology and Chemistry. 2023; 51(1): 37 - 50. 10.15671/hjbc.868396
IEEE Uyar T,Erdamar A,Gümüşderelioğlu M,Aksahin M,Family Name Deactivated G,EROGUL O "Automated Cell Viability Analysis in Tissue Scaffolds." Hacettepe Journal of Biology and Chemistry, 51, ss.37 - 50, 2023. 10.15671/hjbc.868396
ISNAD Uyar, Tansel vd. "Automated Cell Viability Analysis in Tissue Scaffolds". Hacettepe Journal of Biology and Chemistry 51/1 (2023), 37-50. https://doi.org/10.15671/hjbc.868396