A main objective of this paper is to extract bleb from the human embryonic stem cells. Blebbing is an important biological indicator in determining the health of human embryonic stem cells (hESC). Especially, areas of a bleb sequence in a video are often used to distinguish two cells blebbing behaviours in HESC; dynamic and apoptotic blessings. Here analyses active contour segmentation method for bleb extraction in hESC videos and introduces a bio-inspired score function to improve the performance in bleb extraction. The full bleb formation consists of bulb expansion and retraction. Blebs change their size and image properties dynamically in both processes and between frames. Therefore, adaptive parameters are needed for each segmentation method. A score function derived from the change of bleb area and orientation between consecutive frames with cuckoo optimization is proposed which provides adaptive parameters for bleb extraction in videos and classified using artificial neural networks (ANN).