Image Analysis in High Content Screening

Title: Image Analysis in High Content Screening

Volume: 12 Issue: 9

Author(s): Antje Niederlein, Felix Meyenhofer, Daniel White and Marc Bickle

Affiliation:

Keywords: Image quality, segmentation, object features, software

Abstract: The field of High Content Screening (HCS) has evolved from a technology used exclusively by the pharmaceutical industry for secondary drug screening, to a technology used for primary drug screening and basic research in academia. The size and the complexity of the screens have been steadily increasing. This is reflected in the fact that the major challenges facing the field at the present are data mining and data storage due to the large amount of data generated during HCS. On the one hand, technological progress of fully automated image acquisition platforms, and on the other hand advances in the field of automated image analysis have made this technology more powerful and more accessible to less specialized users. Image analysis solutions for many biological problems exist and more are being developed to increase both the quality and the quantity of data extracted from the images acquired during the screens. We highlight in this review some of the major challenges facing automatic high throughput image analysis and present some of the software solutions available on the market or from academic open source solutions.

Cite this article as:

Niederlein Antje, Meyenhofer Felix, White Daniel and Bickle Marc, Image Analysis in High Content Screening, Combinatorial Chemistry & High Throughput Screening 2009; 12 (9) . https://dx.doi.org/10.2174/138620709789383213