Dendritic spine shape analysis based on two-photon microscopy images

Neuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. Previously, volumes of dendritic spines have been considered as a primary parameter to study spine morphology and gain insight into structure-function coupling. However, this reductionist approach fails to incorporate the broad spine structure repertoire. First step towards integrating the rich spine morphology information into functional coupling is to classify spine shapes into main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is currently performed manually, which is a time-intensive task and prone to subjectivity. Availability of automated spine shape analysis tools can accelerate this process and help neuroscientists understand underlying structure and function relationship. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. This thesis focuses on morphological, shape, and appearance features based methods to perform dendritic spine shape analysis using both clustering and classification approaches. We apply manifold learning methods for dendritic spine classification and observe that ISOMAP implicitly computes prominent features suitable for classification purposes. We also apply linear representation based approach for spine classification and conclude that sparse representation provides slightly better classification performance. We propose 2D and 3D morphological features based approach for spine shape analysis and demonstrate the advantage of 3D morphological features. We also use a deep learning based approach for spine classification and show that mid-level features extracted from Convolutional Neural Networks (CNNs) perform as well as hand-crafted features. We propose a kernel density estimation (KDE) based framework for dendritic spine classification. We evaluate our proposed approaches by comparing labels assigned by a neuroscience expert. Our KDE based framework also enables neuroscientists to analyze separability of spine shape classes in the likelihood ratio space, which leads to further insights about the nature of the spine shape analysis problem. Furthermore, we also propose a methodology for unsupervised learning and clustering of spine shapes. In particular, we use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). The objective of clustering in this context is two-fold: confirm the hypothesis of some distinct shape classes and discover new natural groups. We observe that although there are many spines which easily fit into the definition of standard shape types (confirming the hypothesis), there are also a significant number of others which do not comply with standard shape types and demonstrate intermediate properties.