Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images

Abstract

With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is facilitating 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 on deep learning has led to the widespread application of computer-visualization techniques for analyzing and mining data from biomedical images. The availability of open-source software packages and the development of novel, trainable deep neural network architectures has led to increased accuracy in cell detection and segmentation algorithms. By automating cell segmentation, 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 in deep learning- and artificial intelligence-based methods of segmentation and data mining of cells in microscopy images of tissue.

Figure 1

Supervised AI computer-vision algorithms are trained using manual ground truth to achieve accurate cell segmentations on a specific domain of image data (left). A trained algorithm can then be deployed to automatically extract quantitative features from microscopy data to answer clinic- and biology-related questions (right). CNN, convolutional neural network; GAN, generative adversarial networks. Fiji, NIH, Bethesda, MD; https://imagej.nih.gov/ij. Ilastik; http://ilastik.org. ImageJ, NIH; http://imagej.nih.gov.ij.

Figure 2

The application of AI to the microscopy of cells requires careful consideration and planning at all stages of the process, including data acquisition, data set curation, selection and deployment of computer-vision methods, and post–deep-learning analysis. If not properly addressed, the challenges associated with each step may limit the scope of the conclusions. DCNN, deep convolutional neural network.