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 images generated will be harder to analyse than images acquired by an operator as they would have chosen other locations to image and focus correctly. On the other hand, the operator might introduce an unconscious bias in his selection by choosing only the cells whose phenotype is most like the one expected before the experiment.
Histology
Histology is a microscopy application where tissue slices are stained and observed under the microscope (typically light microscope, but electron microscopy is also used).
When using a light microscope, unlike the case of fluorescent imaging, images are typically acquired using standard color camera-systems. This reflects partially the history of the field, where humans were often interpreting the images, but also the fact that the sample can be illuminated with white light and all light collected rather than having to excite fluorophores. When more than one dye is used, a necessary preprocessing step is to unmix the channels and recover an estimate of the pure dye-specific intensities.
It has been shown that the subcellular location of stained proteins can be identified from histology images.
If the goal is a medical diagnostic, then histology applications will often fall into the realm of digital pathology or automated tissue image analysis, which are sister fields of bioimage informatics. The same computational techniques are often applicable, but the goals are medically- rather than research-oriented.
Important Problems
Subcellular Location Analysis
Subcellular location analysis was one of the initial problems in this field. In its supervised mode, the problem is to learn a classifier that can recognize images from the major cell organelles based on images.