Automated Cell Viability Analysis in Tissue Scaffolds

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