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
2753351808 2167510172 2949362008 2076927398
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. We address a central problem of neuroanatomy, namely, the automatic segmentation of neuronal structures depicted in stacks of electron microscopy (EM) images. This is necessary to efficiently map 3D brain structure and connectivity. To segment biological neuron membranes, we use a special type of deep artificial neural network as a pixel classifier. The label of each pixel (membrane or non-membrane) is predicted from raw pixel values in a square window centered on it. The input layer maps each window pixel to a neuron. It is followed by a succession of convolutional and max-pooling layers which preserve 2D information and extract features with increasing levels of abstraction. The output layer produces a calibrated probability for each class. The classifier is trained by plain gradient descent on a 512 × 512 × 30 stack with known ground truth, and tested on a stack of the same size (ground truth unknown to the authors) by the organizers of the ISBI 2012 EM Segmentation Challenge. Even without problem-specific postprocessing, our approach outperforms competing techniques by a large margin in all three considered metrics, i.e. rand error, warping error and pixel error. For pixel error, our approach is the only one outperforming a second human observer. Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN. Background Automated segmentation of fluorescently-labeled cell nuclei in 3D confocal microscope images is essential to many studies involving morphological and functional analysis. A common source of segmentation error is tight clustering of nuclei. There is a compelling need to minimize these errors for constructing highly automated scoring systems. Methods A combination of two approaches is presented. First, an improved distance transform combining intensity gradients and geometric distance is used for the watershed step. Second, an explicit mathematical model for the anatomic characteristics of cell nuclei such as size and shape measures is incorporated. This model is constructed automatically from the data. Deliberate initial over-segmentation of the image data is performed, followed by statistical model-based merging. A confidence score is computed for each detected nucleus, measuring how well the nucleus fits the model. This is used in combination with the intensity gradient to control the merge decisions. Results Experimental validation on a set of rodent brain cell images showed 97 concordance with the human observer and significant improvement over prior methods. Conclusions Combining a gradient-weighted distance transform with a richer morphometric model significantly improves the accuracy of automated segmentation and FISH analysis. Cytometry Part A 56A:23–36, 2003. © 2003 Wiley-Liss, Inc.