Automated Extraction of Brain Cell Layers using Cell Networks and Active Machine Learning

The cortex of the brain consists of layers of cells which differ in morphology and their connectivity to the inner parts of the brain. Different areas of the cortex have different laminar structures based on the functionality of their layers. Hence, delineation of the cortical layers is a major step in the analysis of the cortical activity. The methods proposed in this thesis advance the state-of-the-art in extracting the structural features of the layers and delineating them.

The thesis contributes methods and tools to quantify the spatial distributional properties of the neurons such as their density and the distance from the cortical surface using efficient graphical data structures. It also presents the application of an actively trained logistic regression classifier to the problem of delineating the layers by classifying the neurons as belonging different layers based on the morphological and spatial properties.

The proposed approach is applied to diverse datasets acquired from different cortical areas and it consistently achieves an accuracy of around 90% in delineating the laminar boundaries. It also proves to perform better than unsupervised learning methods and to be more efficient and accurate compared to unaided manual training of the classifier. The proposed methods are integrated into the FARSIGHT toolkit and can be readily used by experts to analyse the cortical layers.