NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel
  Classification for Enhanced Neuronal Cell Instance Segmentation in
  Nissl-Stained Histological Images
Valentina Vadori Antonella Peruffo Jean-Marie Graïc Livio Finos Livio Corain Enrico Grisan
Deep learning has proven to be more effective than other methods in medical
image analysis, including the seemingly simple but challenging task of
segmenting individual cells, an essential step for many biological studies.
Comparative neuroanatomy studies are an example where the instance segmentation
of neuronal cells is crucial for cytoarchitecture characterization. This paper
presents an end-to-end framework to automatically segment single neuronal cells
in Nissl-stained histological images of the brain, thus aiming to enable solid
morphological and structural analyses for the investigation of changes in the
brain cytoarchitecture. A U-Net-like architecture with an EfficientNet as the
encoder and two decoding branches is exploited to regress four color gradient
maps and classify pixels into contours between touching cells, cell bodies, or
background. The decoding branches are connected through attention gates to
share relevant features, and their outputs are combined to return the instance
segmentation of the cells. The method was tested on images of the cerebral
cortex and cerebellum, outperforming other recent deep-learning-based
approaches for the instance segmentation of cells.