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 facilitates distinction between cortical layers. We develop and analyze features of individual neurons to investigate changes in architectonic differentiation and present a novel high-performance, automated tree-ensemble method trained on data manually labeled by three human investigators. From the location and basic measures of neurons, more complex features are developed and used in machine learning models for automatic segmentation of cortical layers. Tree ensembles are used on data manually labeled by three human experts. The most accurate classification results were obtained by training three models separately and creating another ensemble by combining probability outputs for final neuron layer classification. Measurement of importances of developed neuron features on both global model level and individual prediction level are obtained.
The first article that uses features and statistics of individual neurons appeared in 2017. In @cite_10 , authors use automatic segmentation of cells in @math m thick Nissl-stained sections of the mouse brain and develop @math shape descriptor features for each cell. After manually setting the upper and lower bounds for thresholding, a binary image is produced, and blobs are classified as glial cells or neurons, which are further subdivided into pyramidal and non-pyramidal. Using profiles across the mouse cortex for each cell type, authors distinguish five cortical layers, merging layers II and III into a single layer. However, only a few hundreds of cells are found on a small patch of tissue labeled by one expert. The method nevertheless offers a novel approach by using shape descriptors of individual cells for cell classification. A year after, use of GLI profiling on a large in 3D dataset was presented @cite_23 . Authors develop automated analysis of the laminar structure of the BigBrain @cite_6 by analysis of GLI peaks to distinguish between the layers. A relation between cytoarchitectonics an in vivo MRI imaging is discussed.
@cite_10 @cite_6 @cite_23
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We present an image processing algorithm for automatic detection of cortex layers and cells from optical microscopy images for Nissl-stained mouse brain. For every layer of cortex we automatically detect a shape and localization of following cortex cells type: neurons of molecular layer, pyramidal neurons, stellate neurons, and astrocytes. The algorithm includes the steps of: preprocessing, neurons and astrocytes localization, neurons classification, refined cortex layer detection, neurons reclassification. For preprocessing we use converting to gray image, Gaussian blurring, converting to black-white image after background removing, and rough estimate of layers. We use morphological operations with variation radius of structure element for neurons localization and neurons classification. Reference brains are indispensable tools in human brain mapping, enabling integration of multimodal data into an anatomically realistic standard space. Available reference brains, however, are restricted to the macroscopic scale and do not provide information on the functionally important microscopic dimension. We created an ultrahigh-resolution three-dimensional (3D) model of a human brain at nearly cellular resolution of 20 micrometers, based on the reconstruction of 7404 histological sections. “BigBrain” is a free, publicly available tool that provides considerable neuroanatomical insight into the human brain, thereby allowing the extraction of microscopic data for modeling and simulation. BigBrain enables testing of hypotheses on optimal path lengths between interconnected cortical regions or on spatial organization of genetic patterning, redefining the traditional neuroanatomy maps such as those of Brodmann and von Economo.