RECURRENT NEURAL NETWORK FOR MONITORING MOUSE EMBRYONIC STEM CELL COLONY IN VITRO USING TIME-LAPSE FLUORESCENCE MICROSCOPY IMAGES

Abstract

Embryonic stem cells represent a cellular resource for basic biological studies and used as medically relevant cells in vitro. Time-lapse fluorescence microscopy images taken during cell culture are frequently used to monitor time-series morphology changes and status transitions of embryonic stem cell (ESC) colonies, and to select culture medium for maintaining ESCs in undifferentiated or early differentiated stages. Recurrent Neural Network (RNN)-based prediction of time-lapse images generated in future culture can be an effective method for monitoring. Because status transitions of ESC colonies are usually complete in a few consecutive frames of time-lapse images, this study proposes a Long Short-Term Memory (LSTM) structure that incorporates a few new images taken from incubators to learn the newest colony morphology and status for a new RNN training and thus generate predicted time-lapse images. Image processing procedures of entropy, bi-lateral filter and convex-hull calculations are implemented to extract colony regions on the images. Morphology changes and status transitions are then calculated using the extracted ESC colonies. The experiment results show predicted time-lapse images in 5h future and the colony morphology and status transition calculation give accurate visualization and quantitative analyses to monitor the mouse ESCs in vitro at the undifferentiated or early differentiated stages.