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. A toy which enables a child to draw pictures by remote manipulation of a writing instrument, including a frame for supporting a sheet of paper, a marker holder for holding a writing instrument over the paper, and a pair of knobs which can be turned by a child to move the holder in each of two perpendicular directions over the paper. The marker holder is mounted for sliding in the "X" direction on a carriage and the carriage is mounted to move in the "Y" direction on the frame. When a "Y" knob is turned by a child, it rotates a pulley which moves a cord that is attached to the carriage to move it in the "Y" position. When an "X" knob is turned, it rotates a square shaft, and a pinion rotatably mounted on the carriage and coupled to the shaft then moves a rack on the marker holder to slide it in the "X" direction. A program in the form of a strip with rows of letters and numbers defining "X" and "Y" locations to which the holder can be moved, defines a picture that can be drawn by a child. 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.