Comparison of an automated pattern analysis machine vision time-lapse system with traditional endpoint measurements in the analysis of cell growth and cytotoxicity

Abstract

Machine vision is an application of computer vision. It both collects visual information and interprets the images. Although the machine obviously does not 'see' in the same sense that humans do, it is possible to acquire visual information and to create programmes to identify relevant image features in an effective and consistent manner. Machine vision is widely applied in industrial automation, but here we describe how we have used it to monitor and interpret data from cell cultures. The machine vision system used (Cell-IQ) consisted of an inbuilt atmosphere-controlled incubator, where cell culture plates were placed during the test. Artificial intelligence (AI) software, which uses machine vision technology, took care of the follow-up analysis of cellular morphological changes. Basic endpoint and staining methods to evaluate the condition of the cells, were conducted in parallel to the machine vision analysis. The results showed that the automated system for pattern analysis of morphological changes yielded comparable results to those obtained by conventional methods. The inbuilt software analysis offers a promising way of evaluating cell growth and various cell phases. The continuous follow-up and label-free analysis, as well as the possibility of measuring multiple parameters simultaneously from the same cell population, were major advantages of this system, as compared to conventional endpoint measurement methodology.