A block diagram of the proposed automated system is shown in Fig 1. As mentioned above, the system interfaces image analysis methods with the V-CNN model for segmenting colonies in order to compute their morphological and textural features for use in their classification by deep learning architecture. Robust segmentation of the colony region prior to classification is beneficial for automating pluripotency. However, the computerized segmentation of colony regions with feeder cells included is more challenging for the subsequent measurement of stem cell characteristics . In this study, the entire colony image was used for the segmentation of the colony region. In the beginning of the process, median filtering was used as a preprocessing step to reduce the background noise and further to preserve the edges of the stem cell regions. This works on the original image object, replacing the center value of the window with the median value of all neighboring pixel values. A median mask size value of 9 × 9 pixels was applied to the original image.