Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells.

ABSTRACT: Pluripotent stem cells can potentially be used in clinical applications as a model for studying disease progress. This tracking of disease-causing events in cells requires constant assessment of the quality of stem cells. Existing approaches are inadequate for robust and automated differentiation of stem cell colonies. In this study, we developed a new model of vector-based convolutional neural network (V-CNN) with respect to extracted features of the induced pluripotent stem cell (iPSC) colony for distinguishing colony characteristics. A transfer function from the feature vectors to the virtual image was generated at the front of the CNN in order for classification of feature vectors of healthy and unhealthy colonies. The robustness of the proposed V-CNN model in distinguishing colonies w

SUBMITTER: Kavitha MS 

PROVIDER: S-EPMC5744970 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature