title: Computer vision for high content screening.
abstract: High Content Screening (HCS) technologies that combine automated fluorescence microscopy with high throughput biotechnology have become powerful systems for studying cell biology and drug screening. These systems can produce more than 100 000 images per day, making their success dependent on automated image analysis. In this review, we describe the steps involved in quantifying microscopy images and different approaches for each step. Typically, individual cells are segmented from the background using a segmentation algorithm. Each cell is then quantified by extracting numerical features, such as area and intensity measurements. As these feature representations are typically high dimensional (>500), modern machine learning algorithms are used to classify, cluster and visualize cells in HCS experiments. Machine learning algorithms that learn feature representations, in addition to the classification or clustering task, have recently advanced the state of the art on several benchmarking tasks in the computer vision community. These techniques have also recently been applied to HCS image analysis.
authors.affiliation: a The Donnelly Centre, University of Toronto , Toronto , ON , Canada and.|b Department of Electrical and Computer Engineering , University of Toronto , Toronto , ON , Canada.
authors.forename: Oren Z
authors.initials: OZ
authors.lastname: Kraus
authors.affiliation: a The Donnelly Centre, University of Toronto , Toronto , ON , Canada and.|b Department of Electrical and Computer Engineering , University of Toronto , Toronto , ON , Canada.
authors.forename: Brendan J
authors.initials: BJ
authors.lastname: Frey
mesh_terms: D000465:Algorithms; D001709:Biotechnology; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D008856:Microscopy, Fluorescence; D012984:Software; D014785:Vision, Ocular
keywords: Cells; classification; deep learning; high content screening; machine learning; microscopy; segmentation