CNN Based iPS Cell Formation Stage Classifier for Human iPS Cell Growth Status Prediction Using Time-lapse Microscopy Images

CNN classifiers using microscopy images to pick up induced pluripotent stem (iPS) cells from a lot of non-iPS cells ware proposed to improves efficiency of human iPS cell production. By selecting iPS-forming cells at early culture stage, it should be more promising to reduce a considerable amount of work. This paper proposes a CNN reprogramming stage classifier trained by respective types (grown into high-, medium, and low-growth status of iPS-like cells) of time-lapse images taken from the completed cell culturing process. These trained CNNs are used to classify a time-lapse image taken in cell culturing as one stage (a number of consecutive frames) of a respective set of trained time-lapse images. The differences between the actual stage with respective classified stages are than calculated to decide which stage of the completed cell this time-step image taken in cell culturing is near to, then to predict the iPS cell growth status that the culturing cells finally grow into. The method achieved the 85% accuracy to predict the growth status of culturing cells two days later, thus can be applicable for early selection suggestion of good iPS-forming cells.