Bioimage informatics is a subfield of bioinformatics and computational biology. It focuses on the use of computational techniques to analyze bioimages, especially cellular and molecular images, at large scale and high throughput. The goal is to obtain useful knowledge out of complicated and heterogeneous image and related metadata. Automated microscopes are able to collect large numbers of images with minimal intervention. This has led to a data explosion, which absolutely requires automatic processing. Additionally, and surprisingly, for several of these tasks, there is evidence that automated systems can perform better than humans. In addition, automated systems are unbiased, unlike human based analysis whose evaluation may (even unconsciously) be influenced by the desired outcome. There has been an increasing focus on developing novel image processing, computer vision, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. Data Modalities Several data collection systems and platforms are used, which require different methods to be handled optimally. Fluorescent Microscopy Fluorescent microscopy allows the direct visualization of molecules at the subcellular level, in both live and fixed cells. Molecules of interest are marked with either green fluorescent protein (GFP), another fluorescent protein, or a fluorescently-labeled antibody. Several types of microscope are regularly used: widefield, confocal, or two-photon. Most microscopy system will also support the collection of time-series (movies). In general, filters are used so that each dye is imaged separately (for example, a blue filter is used to image Hoechst, then rapidly switched to a green filter to image GFP). For consumption, the images are often displayed in false color by showing each channel in a different color, but these may not even be related to the original wavelengths used. In some cases, the original image could even have been acquired in non-visible wavelengths (infrared is common). The choices at the image acquisition stage will influence the analysis and often require special processing. Confocal stacks will require 3D processing and widefield pseudo-stacks will often benefit from digital deconvolution to remove the out-of-focus light. The advent of automated microscopes that can acquire many images automatically is one of the reasons why analysis cannot be done by eye (otherwise, annotation would rapidly become the research bottleneck). Using automated microscopes means that some images might be out-of-focus (automated focus finding systems may sometimes be incorrect), contain a small number of cells, or be filled with debris. Therefore, the