Cell Nuclei Segmentation in Divergent Images Using Deep Learning and Stochastic Processing

Traditional cell nucleus detection relies on pathologists with microscopes, which is a tedious, costly and time-consuming progress. We develop a deep learning and stochastic processing method to auto-segment those microscopy images, named as Quick-in-process(Qip)-Net. Qip-Net was proposed as an automated method to detect cell nucleus under various conditions, such as randomized cell types, different magnifications, and varying image backgrounds. The network is constructed based on regions with convolution neural network features (RCNN). It is trained by 663 original images and their corresponding masks from Kaggle website. The results showed that Qip-Net could rapidly segment the cell nuclei from the testing dataset of complex and disruptive surroundings with better S-2 score around 3% compared to U-Net.