[2I4-GS-10] AI application: detection

[2I4-GS-10-05] Beyond cell counting: A mixed approach to understand cell activity in phase contrast microscope images

〇Stefan Baar¹, Masahiro Kuragano¹, Kiyotaka Tokuraku¹, Shinya Watanabe¹ (1. Muroran Institute of Technology)

Keywords:deep learning , image segmentation, cell tracking, automation, cell activity estimation

Precisely characterizing cell activity is an important factor when evaluating potential cures for life-threatening diseases such as Alzheimer’s and cancer. It requires precisely registering the time-dependent cell location and especially cell morphology. In bright field black and white images, many cultured cells are especially challenging to distinguish from the background, contaminates, and within adhesive clusters of cells. A mixed approach, consisting of deep learning and physics-based methods, is used to estimate the cell response within a variety of in-house produced datasets. In this research, the morphology and motility evolution of human neuroblastoma, SH-SY5Y cells, in low-contrast time-lapse observations was evaluated. Temporal cell nuclei analysis was performed to aid cell separation from clusters and contaminants. This improves the segmentation results to achieve high mAP (>0.95) at high values for IoU (>0.8) and permits comparable cell activity characterization.