A fast, fully automated cell segmentation algorithm for high‐throughput and high‐content screening

Abstract

High-throughput, high-content screening (HT-HCS) of large compound libraries for drug discovery imposes new constraints on image analysis algorithms. Time and robustness are paramount while accuracy is intrinsically statistical. In this article, a fast and fully automated algorithm for cell segmentation is proposed. The algorithm is based on a strong attachment to the data that provide robustness and have been validated on the HT-HCS of large compound libraries and different biological assays. We present the algorithm and its performance, a description of its advantages and limitations, and a discussion of its range of application. ' 2008 International Society for Advancement of CytometryKey terms image analysis; biological image processing; automation; cytometry; object detection; segmentation; high-content screening AUTOMATED fluorescent microscopy and high-performance computing have allowed the emergence of high-content screening (HCS) as a useful tool in the early stages of drug discovery (1-4). The multidimensional information (''high content'' in HCS) allows for the tackling of biological models inaccessible to unidimensional high-throughput screening (HTS). HCS can also measure multiple effects in a single experiment; for example, the effect of a drug on bacteria (virus, receptor, etc.) and its toxicity on the host target (1,4). HCS therefore has a potential to become a risk/delay/ cost reducer for the later stages of drug development. The last few years have seen a huge increase in image acquisition capacity. Automated fluorescent microscopes can now record more than 40,000 images a day (90 Gb/day), and do so for weeks at a time. HCS is therefore truly becoming high throughput. This evolution introduces fundamental differences with former HCS approaches: (i) The number of images acquired during a HT-HCS campaign [%half a million (4)] requires a fully automated image analysis. (ii) For image analysis not to become the bottleneck of HT-HCS, the rate of image acquisition (%2 s per image) imposes the rate of image analysis. (iii) The amount of data forbids visual control. Results have meaning in a statistical way and must be weighted against a statistically based acceptance criteria. (iv) The measure of quality for a HT-HCS algorithm is a trade off between speed on the one hand and accuracy and robustness on the other. PREVIOUS WORKNumerous HT-HCS applications require a stage of cell segmentation. It most often serves as a basis for subsequent operations, more diverse and specific to a biological assay. While manual (5,6) or semiautomatic (7) cell segmentation methods may be used for HCS, the need for speed and repeatability forbids them for HT-HCS. Most of the cell segmentation algorithms commonly used in HCS suffer from one or many drawbacks that make them ill adapted to HT-HCS use. They may be slow when compared with the

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