Dendritic Spine Classification using Shape and Appearance Features based on Two-Photon Microscopy

Publication date: Available online 18 December 2016
Source:Journal of Neuroscience Methods
Author(s): Muhammad Usman Ghani, Fitsum Mesadi, Sümeyra Demir Kanık, Ali Özgür Argunşah, Anna Felicity Hobbiss, Inbal Israely, Devrim Ünay, Tolga Taşdizen, Müjdat Çetin
BackgroundNeuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. The first step towards understanding the structure-function relationships is to classify spine shapes into the main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is mostly performed manually, which is a time-intensive task and prone to subjectivity.New MethodWe propose an automated method to classify dendritic spines using shape and appearance features based on challenging two-photon laser scanning microscopy (2PLSM) data. Disjunctive Normal Shape Models (DNSM) is a recently proposed parametric shape representation. We perform segmentation of spine images by applying DNSM and use the resulting representation as shape features. Furthermore, we use Histogram of Oriented Gradients (HOG) to extract appearance features. In this context, we propose a kernel density estimation (KDE) based framework for dendritic spine classification, which uses these shape and appearance features.ResultsOur shape and appearance features based approach combined with Neural Network (NN) correctly classifies 87.06% of spines on a dataset of 456 spines.Comparison with Existing MethodsOur proposed method outperforms standard morphological feature based approaches. Our KDE based framework also enables neuroscientists to analyze the separability of spine shape classes in the likelihood ratio space, which leads to further insights about nature of the spine shape analysis problem.ConclusionsResults validate that performance of our proposed approach is comparable to a human expert. It also enable neuroscientists to study shape statistics in the likelihood ratio space.



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