in this area and research is ongoing. High-Content Screening High throughput screens using automated imaging technology (sometimes called high-content screening) have become a standard method for both drug discovery and basic biological research. Using multi-well plates, robotics, and automated microscopy, the same assay can be applied to a large library of possible reagents (typically either small molecules or RNAi) very rapidly, obtaining thousands of images in a short amount of time. Due to the high volume of data generated, automatic image analysis is a necessity. When positive and negative controls are available, the problem can be approached as a classification problem and the same techniques of feature computation and classification that are used for subcellular location analysis can be applied. Segmentation Segmentation of cells is an important sub-problem in many of the fields below (and sometimes useful on its own if the goal is only to obtain a cell count in a viability assay). The goal is to identify the boundaries of cells in a multi-cell image. This allows for processing each cell individually to measure parameters. In 3D data, segmentation must be performed in 3D space. As the imaging of a nuclear marker is common across many images, a widely used protocol is to segment the nuclei. This can be useful by itself if nuclear measurements are needed or it can serve to seed a watershed which extends the segmentation to the whole image. All major segmentation methods have been reported on cell images, from simple thresholding to level set methods. Because there are multiple image modalities and different cell types, each of which implies different tradeoffs, there is no single accepted solution for this problem. Cell image segmentation as an important procedure is often used to study gene expression and colocalization relationship etc. of individual cells. In such cases of single-cell analysis it is often needed to uniquely determine the identities of cells while segmenting the cells. Such a recognition task is often non-trivial computationally. For model organisms such as C. elegans that have well-defined cell lineages, it is possible to explicitly recognize the cell identities via image analysis, by combining both image segmentation and pattern recognition methods. Simultaneous segmentation and recognition of cells has also been proposed as a more accurate solution for this problem when an "atlas" or other prior information of cells is available. Since gene expression at single cell resolution can be obtained using