Cluster computing for digital microscopy

Abstract

Microscopy is becoming increasingly digital and dependent on computation. Some of the computational tasks in microscopy are computationally intense, such as image restoration (deconvolution), some optical calculations, image segmentation, and image analysis. Several modern microscope technologies enable the acquisition of very large data sets. 3D imaging of live cells over time, multispectral imaging, very large tiled 3D images of thick samples, or images from high throughput biology all can produce extremely large images. These large data sets place a very large burden on laboratory computer resources. This combination of computationally intensive tasks and larger data sizes can easily exceed the capability of single personal computers. The large multiprocessor computers that are the traditional technology for larger tasks are too expensive for most laboratories. An alternative approach is to use a number of inexpensive personal computers as a cluster; that is, use multiple networked computers programmed to run the problem in parallel on all the computers in the cluster. By the use of relatively inexpensive over-the-counter hardware and open source software, this approach can be much more cost effective for many tasks. We discuss the different computer architectures available, and their advantages and disadvantages.