KaydedilenlerDerginizi ekleyin

Dergiler / Hacettepe Journal of Biology and Chemistry / 2023 / Cilt: 51 - Sayı: 1

Automated Cell Viability Analysis in Tissue Scaffolds

Tansel UYAR
Aykut ERDAMAR
Menemşe GÜMÜŞDERELİOĞLU
Mehmet Feyzi AKŞAHİN
Gülseren IRMAK
Osman EROĞUL
Sayfa
37–50
Yayın Tarihi
2023-01-01
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

As a result, maximum cell viabilities were obtained at intervals of 63 µm and 90 µm from the scaffold surface.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Image processing techniques are frequently used for extracting quantitative information (cell area, cell size, cell counting, etc.) from different types of microscopic images.
📊
Temel Bulgular & Veri: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.
💡
Sonuç & Katkı:As a result, maximum cell viabilities were obtained at intervals of 63 µm and 90 µm from the scaffold surface.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:image correlation · edge detection · cell area counting

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.

🔖

Bu Makaleye Atıf Yap

UYAR, T., ERDAMAR, A., GÜMÜŞDERELİOĞLU, M., AKŞAHİN, M. F., IRMAK, G., EROĞUL, O. (2023). Automated Cell Viability Analysis in Tissue Scaffolds. Hacettepe Journal of Biology and Chemistry, Cilt: 51 - Sayı: 1, 37–50. https://ajindex.com/articles/669105

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (14)

  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.