Computational analysis of laminar structure of the human cortex based on local neuron features
This work addresses the challenge of automated histological image analysis for neuroscience researchers, but it is incremental as it builds on existing methods with a focus on neuron-level features.
The paper tackled the problem of analyzing and segmenting the laminar structure of the human cortex by developing an automated framework using local neuron features like density and size, trained on manually labeled data from three experts, and achieved results through a machine learning model that combined probability outputs from separate models for each expert.
In this paper, we present a novel method for analysis and segmentation of laminar structure of the cortex based on tissue characteristics whose change across the gray matter underlies distinctive between cortical layers. We develop and analyze features of individual neurons to investigate changes in cytoarchitectonic differentiation and present a novel high-performance, automated framework for neuron-level histological image analysis. Local tissue and cell descriptors such as density, neuron size and other measures are used for development of more complex neuron features used in machine learning model trained on data manually labeled by three human experts. Final neuron layer classifications were obtained by training a separate model for each expert and combining their probability outputs. Importances of developed neuron features on both global model level and individual prediction level are presented and discussed.