MR-NOM: Multi-scale Resolution of Neuronal cells in Nissl-stained histological slices via deliberate Over-segmentation and Merging
This work addresses a critical need in comparative neuroanatomy for accurate cell segmentation to analyze brain cytoarchitecture, though it appears incremental as it builds on existing segmentation techniques.
The paper tackles the challenging problem of automatic instance segmentation of brain cells in Nissl-stained histological images by developing MR-NOM, a method that uses multi-scale over-segmentation and merging based on shape, structure, and intensity features, achieving better performance than two state-of-the-art methods.
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.