MR-NOM: Multi-scale Resolution of Neuronal cells in Nissl-stained histological slices via deliberate Over-segmentation and Merging

TL;DR

MR-NOM is a novel multi-scale method for automatic neuron cell segmentation in Nissl-stained brain images, combining over-segmentation and merging to improve accuracy over existing techniques.

Contribution

It introduces a multi-scale over-segmentation and merging approach with a classifier for improved neuron segmentation in histological images.

Findings

Outperforms two state-of-the-art segmentation methods.

Effectively handles overlapping and touching cells.

Proven successful on cerebral cortex images.

Abstract

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.