There are several reasons why a (reasonably good) segmentation can improve the analysis and visualization of your data. For instance, BrainVoyager QX and FreeSurfer support the matching of cortical brain surface representations (meshes) to an average group mesh, which means that the localization of functional activations (cluster peaks) can be usually performed with higher precision within the nomenclature of anatomical regions (not necessarily w.r.t. stereotactic coordinates of a template space). Another reason is that the visualization of results of a single subject on a surface (or group results on the surface representation of a representative subject) can improve the “readability” and comprehensiveness of those results.
While several segmentation algorithms exist, it is quite common that some amount of manual work has to be put into the segmentation to cope with the following problems (among others):
The following steps are potentially beneficial for segmentation when working with anatomical datasets: