Dendritic spine classification using shape and appearance features based on two-photon microscopy

Ghani, Muhammad Usman and Mesadi, Fitsum 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 (2017) Dendritic spine classification using shape and appearance features based on two-photon microscopy. Journal of Neuroscience Methods, 279 . pp. 13-21. ISSN 0165-0270 (Print) 1872-678X (Online)

This is the latest version of this item.

[thumbnail of Dendritic_spine_classification_using_shape_and_appearance_features_based_on_two-photon_microscopy.pdf] PDF
Restricted to Registered users only

Download (1MB) | Request a copy


Background: Neuronal 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 method: We 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. Results: Our 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 methods: Our 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. Conclusions: Results 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.
Item Type: Article
Uncontrolled Keywords: Dendritic spines; Classification; Disjunctive Normal Shape Model; Histogram of oriented gradients; Shape analysis; Kernel density estimation; Microscopy
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Müjdat Çetin
Date Deposited: 09 Sep 2017 12:44
Last Modified: 26 Apr 2022 09:51

Available Versions of this Item

Actions (login required)

View Item
View Item