Browsing by Author "Erdil, Ertunc"

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Citation - WoS: 12
Citation - Scopus: 14
An interactive time series image analysis software for dendritic spines
(Nature Portfolio, 2022) Argunsah, Ali Ozgur; Erdil, Ertunc; Ghani, Muhammad Usman; Ramiro-Cortes, Yazmin; Hobbiss, Anna F.; Karayannis, Theofanis; Cetin, Mujdat
Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios.
Citation - WoS: 17
Citation - Scopus: 17
Nonparametric Joint Shape and Feature Priors for Image Segmentation
(IEEE-Inst Electrical Electronics Engineers Inc, 2017) Erdil, Ertunc; Ghani, Muhammad Usman; Rada, Lavdie; Argunsah, Ali Ozgur; Unay, Devrim; Tasdizen, Tolga; Cetin, Mujdat
In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes ( classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape-and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method.
Citation - WoS: 3
Citation - Scopus: 3
Nonparametric Joint Shape and Feature Priors for Segmentation of Dendritic Spines
(IEEE, 2016) Erdil, Ertunc; Rada, Lavdie; Argunsah, A. Ozgur; Israely, Inbal; Unay, Devrim; Tasdizen, Tolga; Cetin, Mujdat
Multimodal shape density estimation is a challenging task in many biomedical image segmentation problems. Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. Such density estimates are only expressed in terms of distances between shapes which may not be sufficient for ensuring accurate segmentation when the observed intensities provide very little information about the object boundaries. In such scenarios, employing additional shape-dependent discriminative features as priors and exploiting both shape and feature priors can aid to the segmentation process. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors using Parzen density estimator. The joint prior density estimate is expressed in terms of distances between shapes and distances between features. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on dendritic spine segmentation in 2-photon microscopy images which involve a multimodal shape density.
Citation - WoS: 6
Citation - Scopus: 7
Tracking-Assisted Detection of Dendritic Spines in Time-Lapse Microscopic Images
(Pergamon-Elsevier Science Ltd, 2018) Rada, Lavdie; Kilic, Bike; Erdil, Ertunc; Ramiro-Cortes, Yazmin; Israely, Inbal; Unay, Devrim; Cetin, Mujdat
Detecting morphological changes of dendritic spines in tim e-lapse microscopy images and correlating them with functional properties such as memory and learning, are fundam ental and challenging problems in neurobiology research. In this paper, we propose an algorithm for dendritic spine detection in time series. The proposed approach initially performs spine detection at each time point and improves the accuracy by exploiting the information obtained from tracking of individual spines over time. To detect dendritic spines in a time point image we em ploy an SVM classifier trained by pre-labeled SIFT feature descriptors in combination with a dot enhancement filter. Second, to track the growth or loss of spines, we apply a SIFT-based rigid registration method for the alignment of tim e-series images. This step takes into account both the structure and the movement of objects, combined with a robust dynamic scheme to link inform ation about spines that disappear and reappear over time. Next, we improve spine detection by em ploying a probabilistic dynam ic program m ing approach to search for an optimum solution to accurately detect missed spines. Finally, we determine the spine location more precisely by performing a watershed-geodesic active contour model. We quantitatively assess the perform ance of the proposed spine detection algorithm based on annotations performed by biologists and com pare its perform ance with the results obtained by the noncommercial software NeuronIQ. Experiments show that our approach can accurately detect and quantify spines in 2-photon m icroscopy tim e-lapse data and is able to accurately identify spine elimination and form ation. (C) 2018 IBRO. Published by Elsevier Ltd. AM rights reserved.