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Publication Metadata only A tool for automatic dendritic spine detection and analysis. Part I: Dendritic spine detection using multi-level region-based segmentation(2012) Erdil, Ertunç; Yağcı, A. Murat; Argunşah, Ali Özgür; Ramiro-Cortés, Yazmín; Hobbiss, Anna Felicity; Israely, Inbal; Ünay, Devrim; Erdil, Ertunç, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Yağcı, A. Murat, Department of Computer Engineering, Boğaziçi Üniversitesi, Bebek, Turkey; Argunşah, Ali Özgür, Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Ramiro-Cortés, Yazmín, Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Hobbiss, Anna Felicity, Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Israely, Inbal, Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Ünay, Devrim, Biomedical Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyWe propose an image processing pipeline for dendritic spine detection in two-photon fluorescence microscopy images. Spines of interest to neuroscientists often contain high intensity regions with respect to their surroundings. We find such maxima regions using morphological image reconstruction. These regions facilitate a multi-level segmentation algorithm to detect spines. First, watershed algorithm is applied to extract initial rough regions of spines. Then, these results are further refined using a graph-theoretic region-growing algorithm which incorporates segmentation on a sparse representation of image data and hierarchical clustering as a post-processing step. We compare our final results to segmentation results of the domain expert. Our pipeline produces promising segmentation results with practical run times for monitoring streaming data. © 2012 IEEE. © 2013 Elsevier B.V., All rights reserved.Publication Metadata only A watershed and active contours based method for dendritic spine segmentation in 2-photon microscopy images, 2-Foton mikroskopi görüntülerindeki dendritik dikenlerin bölütlenmesi için watershed ve etkin çevritlere dayali bir yöntem(2013) Erdil, Ertunç; Argunşah, Ali Özgür; Ünay, Devrim; Çetin, Müjdat; Erdil, Ertunç, Mühendislik ve Doǧa Bilimleri Fakültesi, Sabancı Üniversitesi, Tuzla, Turkey; Argunşah, Ali Özgür, Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Ünay, Devrim, Bahçeşehir Üniversitesi, Istanbul, Turkey; Çetin, Müjdat, Mühendislik ve Doǧa Bilimleri Fakültesi, Sabancı Üniversitesi, Tuzla, TurkeyAnalysing morphological and volumetric properties of dendritic spines from 2-photon microscopy images has been of interest to neuroscientists in recent years. Developing robust and reliable tools for automatic analysis depends on the segmentation quality. In this paper, we propose a new segmentation algorithm for dendritic spine segmentation based on watershed and active contour methods. First, our proposed method coarsely segments the dendritic spine area using the watershed algorithm. Then, these results are further refined using a region-based active contour approach. We compare our results and the results of existing methods in the literature to manual delineations of a domain expert. Experimental results demonstrate that our proposed method produces more accurate results than the existing algorithms proposed for dendritic spine segmentation. © 2013 IEEE. © 2013 Elsevier B.V., All rights reserved.Publication Metadata only Biomedical image time series registration with particle filtering, Parçacik süzgeci ile biyomedikal görüntü zaman serisi çakiştirma(2013) Yaǧci, Murat; Erdil, Ertunç; Argunşah, Ali Özgür; Ünay, Devrim; Çetin, Müjdat; Akarun, Lale; Gürgen, Fïkret S.; Yaǧci, Murat, Boğaziçi Üniversitesi, Bebek, Turkey; Erdil, Ertunç, Mühendislik ve Doǧa Bilimleri Fakültesi, Sabancı Üniversitesi, Tuzla, Turkey; Argunşah, Ali Özgür, Champalimaud Centre for the Unknown, Lisbon, Portugal; Ünay, Devrim, Bahçeşehir Üniversitesi, Istanbul, Turkey; Çetin, Müjdat, Mühendislik ve Doǧa Bilimleri Fakültesi, Sabancı Üniversitesi, Tuzla, Turkey; Akarun, Lale, Boğaziçi Üniversitesi, Bebek, Turkey; Gürgen, Fïkret S., Boğaziçi Üniversitesi, Bebek, TurkeyWe propose a family of methods for biomedical image time series registration based on Particle filtering. The first method applies an intensity-based information-theoretic approach to calculate importance weights. An effective second group of methods use landmark-based approaches for the same purpose by automatically detecting intensity maxima or SIFT interest points from image time series. A brute-force search for the best alignment usually produces good results with proper cost functions, but becomes computationally expensive if the whole search space is explored. Hill climbing optimizations seek local optima. Particle filtering avoids local solutions by introducing randomness and sequentially updating the posterior distribution representing probable solutions. Thus, it can be more robust for the registration of image time series. We show promising preliminary results on dendrite image time series. © 2013 IEEE. © 2013 Elsevier B.V., All rights reserved.Publication Metadata only Automatic dendritic spine detection using multiscale dot enhancement filters and SIFT features(Institute of Electrical and Electronics Engineers Inc., 2014) Rada, Lavdie; Erdil, Ertunç; Ozgurargunsah, A.; Ünay, Devrim; Çetin, Müjdat; Rada, Lavdie, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Erdil, Ertunç, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Ozgurargunsah, A., Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Ünay, Devrim, Department of Biomedical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Çetin, Müjdat, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, TurkeyStatistical characterization of morphological changes of dendritic spines is becoming of crucial interest in the field of neurobiology. Automatic detection and segmentation of dendritic spines promises significant reductions on the time spent by the scientists and reduces the subjectivity concerns. In this paper, we present two approaches for automated detection of dendritic spines in 2-photon laser scanning microscopy (2pLSM) images. The first method combines the idea of dot enhancement filters with information from the dendritic skeleton. The second method learns an SVM classifier by utilizing some pre-labeled SIFT feature descriptors and uses the classifier to detect dendritic spines in new images. For the segmentation of detected spines, we employ a watershed-variational segmentation algorithm. We evaluate the proposed approaches by comparing with manual segmentations of domain experts and the results of a noncommercial software, NeuronIQ. Our methods produce promising detection rate with high segmentation accuracy thus can serve as a useful tool for spine analysis. © 2015 Elsevier B.V., All rights reserved.Publication Metadata only Automated dendritic spine tracking on 2-photon microscopic images, 2-Foton Mikroskopi Goriintiilerinde Otomatik Dendritik Diken Takibi(Institute of Electrical and Electronics Engineers Inc., 2015) Kilic, Bike; Rada, Lavdie; Erdil, Ertunç; Argunşah, Ali Özgür; Çetin, Müjdat; Ünay, Devrim; Kilic, Bike, Bahçeşehir Üniversitesi, Istanbul, Turkey; Rada, Lavdie, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdil, Ertunç, Sabancı Üniversitesi, Tuzla, Turkey; Argunşah, Ali Özgür, Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Çetin, Müjdat, Sabancı Üniversitesi, Tuzla, Turkey; Ünay, Devrim, Bahçeşehir Üniversitesi, Istanbul, TurkeyThe rapid and spontaneous morphological changes of dendritic spines have been an important observation to understand how information is stored in brain. Manual assessment of spine structure has been a useful tool to understand the differences between wild type (normal) and diseased cases. In order to perform a more through analysis, automatic tools need to be developed due to the immense amount of image data collected throughout the experiments. Additionally, dendritic spines are very dynamic structures and florescence microscopy contains high level of noise, blur and shift due to the optical properties. In this study, we track locations of dendritic spines in a full series of a time-lapse two photon microscopic images. To achieve this we propose a combined detection and tracking framework. For the detection we use a SIFT based algorithm, while the tracking requires a combination of registration and distance based spine matching. Experimental results show that this technique helps to track detected spines in time series even though the noise or blur deformed the image and complicated the detection. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only A joint classification and segmentation approach for dendritic spine segmentation in 2-photon microscopy images(IEEE Computer Society, 2015) Erdil, Ertunç; Argunşah, Ali Özgür; Tasdizen, Tolga; Ünay, Devrim; Çetin, Müjdat; Erdil, Ertunç, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Argunşah, Ali Özgür, Champalimaud Centre for the Unknown, Lisbon, Portugal; Tasdizen, Tolga, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey, John and Marcia Price College of Engineering, Salt Lake City, United States; Ünay, Devrim, Department of Biomedical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Çetin, Müjdat, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, TurkeyShape priors have been successfully used in challenging biomedical imaging problems. However when the shape distribution involves multiple shape classes, leading to a multimodal shape density, effective use of shape priors in segmentation becomes more challenging. In such scenarios, knowing the class of the shape can aid the segmentation process, which is of course unknown a priori. In this paper, we propose a joint classification and segmentation approach for dendritic spine segmentation which infers the class of the spine during segmentation and adapts the remaining segmentation process accordingly. We evaluate our proposed approach on 2-photon microscopy images containing dendritic spines and compare its performance quantitatively to an existing approach based on nonparametric shape priors. Both visual and quantitative results demonstrate the effectiveness of our approach in dendritic spine segmentation. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only Nonparametric joint shape and feature priors for segmentation of dendritic spines(IEEE Computer Society [email protected], 2016) Erdil, Ertunç; Rada, Lavdie; Argunşah, Ali Özgür; Israely, Inbal; Ünay, Devrim; Tasdizen, Tolga; Çetin, Müjdat; Erdil, Ertunç, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Rada, Lavdie, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Argunşah, Ali Özgür, Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Israely, Inbal, Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Ünay, Devrim, Department of Electrical and Electronic Engineering, Izmir Ekonomi Üniversitesi, Izmir, Turkey; Tasdizen, Tolga, John and Marcia Price College of Engineering, Salt Lake City, United States; Çetin, Müjdat, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, TurkeyMultimodal 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. © 2016 Elsevier B.V., All rights reserved.Publication Metadata only Coupled shape priors for dynamic segmentation of dendritic spines(Institute of Electrical and Electronics Engineers Inc., 2017) Atabakilachini, Naeimeh; Erdil, Ertunç; Argunşah, Ali Özgür; Rada, Lavdie; Ünay, Devrim; Çetin, Müjdat; Atabakilachini, Naeimeh, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Erdil, Ertunç, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Argunşah, Ali Özgür, University of Zurich, Brain Research Institute, Zurich, Switzerland; Rada, Lavdie, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ünay, Devrim, Department of Biomedical Engineering, Izmir Ekonomi Üniversitesi, Izmir, Turkey; Çetin, Müjdat, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, TurkeySegmentation of biomedical images is a challenging task, especially when there is low quality or missing data. The use of prior information can provide significant assistance for obtaining more accurate results. In this paper we propose a new approach for dendritic spine segmentation from microscopic images over time, which is motivated by incorporating shape information from previous time points to segment a spine in the current time point. In particular, using a training set consisting of spines in two consecutive time points to construct coupled shape priors, and given the segmentation in the previous time point, we can improve the segmentation process of the spine in the current time point. Our approach has been evaluated on 2-photon microscopy images of dendritic spines and its effectiveness has been demonstrated by both visual and quantitative results. © 2017 Elsevier B.V., All rights reserved.