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Image processing methods have opened the opportunity to extract quantitative information from confocal microscopy images of biological samples, dramatically increasing the range of questions that can be addressed experimentally in biology. Biologists aim to understand how cells behave and what genes do to build a normal animal, and what goes wrong in disease or upon injury. For this, they look at how alterations in gene function and application of drugs affect tissue, organ or whole body integrity, using confocal microscopy images of samples stained with cell specific markers. Image-processing methods have enormous potential to extract information from this kind of samples, but surprisingly, they are still relatively underexploited. One useful parameter to quantify is cell number. Cell number is the balance between cell division and cell death; it is controlled tightly during growth and it can be altered in disease, most notoriously neurodegeneration and cancer. Injury (e.g. spinal cord injury) results in an increase in cell death, plus a homeostatic regulation of cell proliferation. Thus to understand normal animal development, injury responses and disease, it is important to find out how many cells die or divide, or how many cells of a given type there are in an organ. Generally, cells are counted using automated methods after dissociating cells from a tissue (e.g. fluorescence-activated cell sorting, FACS, based), or when they are distributed in a dish in cell culture experiments, using image processing techniques in 2D (e.g. using Metamorph software). However, these approaches alter the normal cellular contexts and the procedures themselves can alter the relative numbers of cells. To maintain information relevant to how genes and cells behave in the organism, it is best to count cells in vivo (i.e. in the intact animal) or at least in an entire organ or tissue (i.e. in situ). Counting in vivo or in situ is generally carried out manually, or it consists of estimates of number of cells stained with a particular cell marker or inferences from anatomical alterations. These methods can be extremely time-consuming, estimates can be inaccurate, and the questions that can be addressed using these methods are limited. Manual counting can be experimentally cumbersome, tedious, labour intensive and error prone. The advent of confocal microscopy, which allows the capture of 3D images, has enabled the development of automatic and semi-automatic image processing methods to count cells in whole tissues or entire small animals. Whereas excellent automated methods

Image Processing Methods for Automatic Cell Counting In Vivo or In Situ Using 3D Confocal Microscopy