Dendritic spine shape analysis: a clustering perspective
Ghani, Muhammad Usman and Erdil, Ertunç and Demir Kanık, Sümeyra Ümmühan and Argunşah, Ali Özgür and Hobbiss, Anna Felicity and Israely, Inbal and Ünay, Devrim and Taşdizen, Tolga and Çetin, Müjdat (2016) Dendritic spine shape analysis: a clustering perspective. In: 14th European Conference on Computer Vision (ECCV 2016), Amsterdam, The Netherlands
Official URL: http://dx.doi.org/10.1007/978-3-319-46604-0_19
Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. 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. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types.
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