Portal de Periódicos da CAPES

Automated Image Analysis for High-Content Screening and Analysis

2010; Elsevier BV; Volume: 15; Issue: 7 Linguagem: Inglês

10.1177/1087057110370894

2472-5560

Aabid Shariff, Joshua Kangas, Luís Pedro Coelho, Shannon Quinn, Robert F. Murphy,

Genetics, Bioinformatics, and Biomedical Research

The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.