Sparse Recovery Methods for Cell Detection and Layer Estimation

Abstract

Robust methods for characterizing the cellular architecture (cytoarchitecture) of the brain are needed to differentiate brain areas, identify neurological diseases, and model architectural differences across species. Current methods for mapping the cytoarchitecture and, in particular, identifying laminar (layer) divisions in tissue samples require the expertise of trained neuroanatomists to manually annotate the various regions-of-interest and cells within an image. However, as neuroanatomical datasets grow in volume, manual annotations become inefficient, impractical, and risk the potential of biasing results. In this paper, we propose an automated framework for cellular detection and density estimation that enables the detection of laminar divisions within retinal and neocortical histology datasets. Our approach for layer detection uses total variation minimization to find a small number of change points in the density that signify the beginning and end of each layer. We apply these methods to micron-scale histology images from a variety of cortical areas of the mouse brain and retina, as well as synthetic datasets. Our results demonstrate the feasibility of using automation to reveal the cytoarchitecture of neurological samples in high-resolution images.