Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.

With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is enabling high-throughput analysis of image data in a variety of biomedical imaging disciplines, ranging from radiology and pathology to cancer biology and immunology. Specifically, a growth in research surrounding deep learning has led to widespread application of computer vision techniques to analyze and mine data from biomedical images. The availability of open-source software packages and the development of novel, trainable deep neural network architectures has led to an increase in accuracy of cell detection and segmentation algorithms. By automating cellular segmentations, it is now possible to mine quantifiable cellular and spatio-cellular features from microscopy images, providing insight into the organization of cells in various pathologies. This mini-review provides an overview of the current state-of-the-art deep learning and artificial intelligence methods for segmentation and data mining of cells in microscopy images of tissue.