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.
Since the first methods for automated or semi-automated analysis of cortical layers were developed, central idea in almost all methods was the use of sampling along transverse lines drawn either manually or semi-automatically @cite_17 across the cortex, perpendicular to the laminar structure and spanning the full width of the cortex @cite_15 , @cite_24 , @cite_28 . At first, changes of optical density, or gray-level index (GLI) was measured by these profiles, which is a crude estimate of changes in neuronal density.
@cite_24 @cite_15 @cite_28 @cite_17
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Functional differences among various portions of the cerebral cortex are often correlated with differing cortical layering patterns. Convenient, accurate techniques for scoring layering should therefore prove useful in electrophysiological as well as anatomical investigations. We report the application of a computer-controlled scanning microdensitometer as a means of rapid measurement of optical densities in histological sections of monkey visual cortices, areas 17 and 18. The technique readily permits recognition of the previously defined cortical layers and suggests that still finer consistent layering patterns exist; it provides objective "fingerprints" of cortical regions which facilitate comparisons of structure from area to area and from animal to animal. The procedure should serve also to score the positions of autoradiographic grains, degenerating axonal terminals, and other labeled structures, and to allow the comparison of preparations stained by various techniques. We describe a new, observer-independent procedure for identifying boundaries between cortical areas. The method is useful for images obtained from sections which provide microstructural information on the cortical laminar pattern, e.g., Nissl-, myelin-, or immunohistochemically stained sections or receptor autoradiographs. The laminar pattern is represented by profile curves extending from the cortical surface to the white matter boundary. These profiles are constructed from digitized images. Digitization is based on the grey level index (Nissl) or densitometry (myelin, immunohistochemistry, receptor autoradiography). The shapes of neighboring profiles are compared by calculating their distances according to feature vectors extracted from the profiles. Profiles derived from a homogeneous area can be expected to be similar in shape and hence show low distance values between each other. Maximum distances can be found between profiles which lie on opposite sides of a structural boundary. The Mahalanobis distance was found to be more sensitive and to yield greater spatial resolution than other distance measures such as the Euclidean distance. Cell-stained sections of the human neocortex were analyzed. The method not only verified boundaries which had been defined by visual inspection, it also revealed new ones which had not been detected visually. The procedure offers an important supplement to the traditional methods based on visual inspection which, for the first time, is based on quantitative data and therefore offers a new level of reproducibility and observer independence. Anatomical atlases based on this procedure thus provide a new tool for the interpretation of structural data obtained from functional imaging techniques. The laminar organization of the cerebral cortex is a fundamental characteristic of the brain, with essential implications for cortical function. Due to the rapidly growing amount of high-resolution brain imaging data, a great demand arises for automated and flexible methods for discriminating the laminar texture of the cortex. Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical laminar organization in high-resolution 2-photon microscopic neural image data of mouse brain without observer bias, that is, without the prerequisite of manually labeled training data. For local cortical foci, we modify an unsupervised clustering approach to identify and represent the laminar cortical structure. Subsequently, supervised machine learning is applied to transfer the resulting layer labels across different locations and image data, to ensure the existence of a consistent layer label system. By using neurobiologically meaningful features, the discrimination results are shown to be consistent with the layer classification of the classical Brodmann scheme, and provide additional insight into the structure of the cerebral cortex and its hierarchical organization. Thus, our work paves a new way for studying the anatomical organization of the cerebral cortex, and potentially its functional organization. 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.