Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10918
Title: Automated Cell Viability Analysis in Tissue Scaffolds
Authors: Gümüşderelioğlu, Menemşe
Eroğul, Osman
Uyar, Tansel
Erdamar, Aykut
Akşahin, Mehmet Fevzi
Irmak, Gülseren
Issue Date: 2023
Abstract: 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.
URI: https://doi.org/10.15671/hjbc.868396
https://search.trdizin.gov.tr/yayin/detay/1200426
https://hdl.handle.net/20.500.11851/10918
ISSN: 1303-5002
2687-475X
Appears in Collections:TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection

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