Investigation of time dependent growth of HepG2 cancerous cells using deep learning and shape metrics

Abstract

Abstract Study of growth of Human hepatocellular carcinoma cells (HepG2) cells provide useful information for clinical study of megestrol acetate for the treatment of Hepatocellular carcinoma. In this study, we analyzed the growth of HepG2 liver cancerous cells using image processing methods. Initially, the HepG2 cells were cultured and microscopic images were captured in bright field mode at time of seeding (00 h) followed by 06 h and 18 h. We segmented the cells using Tsallis and deep learning methods and the average size of colonies were calculated using shape metrics. Finally, we correlated the cell density obtained using MTT assay with the average size of colonies calculated from the Tsallis and deep learning segmented images. Results show that deep learning methods were able to segment the cells more precisely than Tsallis method. The average colony area calculated from the deep learning segmented images increases with time and concentration. The cell growth and adhesion pattern measured by deep leaning method showed good consistency with spectroscopic observations. The process pipeline provides a new way to assess cell adhesion and proliferation with capabilities in measuring their occupied surface area. The framework documented can be a promising tool to automate cancerous cell growth by evaluating average colony size for studying normal and pathological conditions.