Browsing by Author "Israely, Inbal"

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Citation - WoS: 36
Citation - Scopus: 36
Dendritic Spine Classification Using Shape and Appearance Features Based on Two-Photon Microscopy
(Elsevier, 2017) Ghani, Muhammad Usman; Mesadi, Fitsum; Kanik, Sumeyra Demir; Argunsah, Ali Oezguer; Hobbiss, Anna Felicity; Israely, Inbal; Unay, Devrim
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. (C) 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 5
Dendritic Spine Shape Analysis Using Disjunctive Normal Shape Models
(IEEE, 2016) Ghani, Muhammad Usman; Mesadi, Fitsum; Kanik, Sumeyra Demir; Argunsah, Ali Ozgur; Israely, Inbal; Unay, Devrim; Tasdizen, Tolga
Analysis of dendritic spines is an essential task to understand the functional behavior of neurons. Their shape variations are known to be closely linked with neuronal activities. Spine shape analysis in particular, can assist neuroscientists to identify this relationship. A novel shape representation has been proposed recently, called Disjunctive Normal Shape Models (DNSM). DNSM is a parametric shape representation and has proven to be successful in several segmentation problems. In this paper, we apply this parametric shape representation as a feature extraction algorithm. Further, we propose a kernel density estimation (KDE) based classification approach for dendritic spine classification. We evaluate our proposed approach on a data set of 242 spines, and observe that it outperforms the classical morphological feature based approach for spine classification. Our probabilistic framework also provides a way to examine the separability of spine shape classes in the likelihood ratio space, which leads to further insights about the nature of the shape analysis problem in this context.
İki-foton Mikroskopi Görüntülerinde Dendrit Dikenlerinin Otomatik Olarak Bölütlendirilmesi, Sını?andırılması ve Takibi için Olasılık ve Makine Öğrenmesi Temelli Yöntemler
(2017) Israely, Inbal; Ghanı, Muhammad Usman; Atabakılachını, Naeimeh; Kılıç, Bike; Ünay, Devrim; Argunşah, Ali Özgür; Çetin, Müjdat
Nöronların dendritleri üzerindeki dikenlerin (spine) yapılarının ve yapısal dinamiklerinin analizi, öğrenme, hafıza oluşumu ve ilgili patolojilerin temelini oluşturan mekanizmaların aydınlatılmasında önem taşımaktadır. Son yıllarda bu yapıları görüntüleyen teknolojilerde elde edilen önemli ilerlemeler sonucunda, analiz edilmesi gereken veri miktarı çok artmıştır. Bahsedilen analizler sinirbilim araştırmacıları tarafından çoğunlukla elle yapıldığından hem çok vakit almakta hem de yorgunluğa/dikkatsizliğe bağlı insan hatası içermektedir. Bunun bir sonucu olarak dikenlerin yapısal, uzamsal, ve zamansal değişimlerini hızlı ve güvenilir bir şekilde analiz etmeye yarayacak görüntü işleme araçlarının geliştirilmesi son yıllarda önemli bir araştırma konusu olarak ortaya çıkmıştır. Buprojeninkonusudinamikplastisiteçalışmalarınaolanaktanıyaniki-fotonmikroskopisi verilerininotomatikolarakişlenmesivebuverilerdenbilgiçıkarılmasıiçinalgoritmageliştirme üzerinedir. Projede dendrit dikenlerinin analizi için olasılık ve makine öğrenmesi temelli yeni görüntü işleme algoritmaları geliştirilmiştir. Daha somut olarak, projede dendrit dikenlerinin iki-foton mikroskopisi görüntülerinden otomatik olarak (1) tespiti, (2) bölütlenmesi, (3) takibi ve dinamik olarak bölütlenmesi, (4) şekil analizi (sını?andırılması ve kümelenmesi) için ayrıayrıvebuproblemlerinbazılarıiçinbirdenfazlaolmaküzereyeniyöntemlerveakademik katkılar üretilmiştir. Projedeki çalışmalara dayalı olarak şu ana kadar 3 dergi makalesi ve 15 konferans bildirisi yayımlanmış, 1 doktora ve 2 yüksek lisans tezi üretilmiştir. Ayrıca 1 dergi makalesi değerlendirme ve 5 dergi makalesi de yazım aşamasındadır. Yayınlara ek olarak ortaya çıkan diğer önemli bir çıktı ise projede geliştirilen analiz yöntemlerini içeren Matlab temelli, kullanıcı dostu gra?k arayüzlü bir yazılım aracıdır. Bu yazılım aracını araştırmacılar projenin http://spines.sabanciuniv.edu/ web sitesi üzerinden temin edip kullanabileceklerdir. Proje Doç.Dr. Devrim Ünay’ın (İzmir Ekonomi Üniversitesi) yürütücülüğünde Sabancı Üniversitesi,BahçeşehirÜniversitesivePortekiz’dekiChampalimaudSinirbilimProgramı’nın ortak çalışmaları ile gerçekleştirilmiştir.
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: 1
Citation - Scopus: 5
On Comparison of Manifold Learning Techniques for Dendritic Spine Classification
(IEEE, 2016) Ghani, Muhammad Usman; Argunsach, Ali Ozgur; Israely, Inbal; Unay, Devrim; Tasdizen, Tolga; Cetin, Mujdat
Dendritic spines are one of the key functional components of neurons. Their morphological changes are correlated with neuronal activity. Neuroscientists study spine shape variations to understand their relation with neuronal activity. Currently this analysis performed manually, the availability of reliable automated tools would assist neuroscientists and accelerate this research. Previously, morphological features based spine analysis has been performed and reported in the literature. In this paper, we explore the idea of using and comparing manifold learning techniques for classifying spine shapes. We start with automatically segmented data and construct our feature vector by stacking and concatenating the columns of images. Further, we apply unsupervised manifold learning algorithms and compare their performance in the context of dendritic spine classification. We achieved 85.95% accuracy on a dataset of 242 automatically segmented mushroom and stubby spines. We also observed that ISOMAP implicitly computes prominent features suitable for classification purposes.
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.