Impact of image segmentation on high-content screening data quality for SK-BR-3 cells

Background: High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of wellsegmented cells. Results: Cases of poor cell body segmentation occurred frequently for the SK-BR-3 cell line. We trained classifiers to identify SK-BR-3 cells that were well segmented. On an independent test set created by h...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Andrew A. Hill, Peter LaPan, Yizheng Li, Steve Haney
• Keywords: BMCBI 2007, Cell Lines, HCS Data, Image