  In comparative neuroanatomy, the characterization of brain cytoarchitecture
is critical to a better understanding of brain structure and function, as it
helps to distill information on the development, evolution, and distinctive
features of different populations. The automatic segmentation of individual
brain cells is a primary prerequisite and yet remains challenging. A new method
(MR-NOM) was developed for the instance segmentation of cells in Nissl-stained
histological images of the brain. MR-NOM exploits a multi-scale approach to
deliberately over-segment the cells into superpixels and subsequently merge
them via a classifier based on shape, structure, and intensity features. The
method was tested on images of the cerebral cortex, proving successful in
dealing with cells of varying characteristics that partially touch or overlap,
showing better performance than two state-of-the-art methods.
