Automated Tracking and Modeling of Microtubule Dynamics

Saban, M.; Altınok, Alphan; Peck, Austin; Kenney, Charles; Feinstein, Stuart C.; Wilson, Leslie; Rose, Kenneth; Manjunath, B.S.

doi:10.1109/isbi.2006.1625097

Cited by 30 publications

(29 citation statements)

References 11 publications

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“…Included among such application are the extraction of roads (e.g., [10,16,21]), the detection of coastlines in satellite images (e.g., [3]), model-based contour detection (e.g., [26,27]), extraction of thin linear features in medical or biological contexts (e.g., [4,12,13,16]), and boundary detection (e.g., [2,17]). …”

Section: Related Workmentioning

confidence: 99%

“…Owing to the type of applications that they have been usually adopted for, most existing active contour models are formulated on closed curves, while much fewer open active models are put to use. Interestingly, however, the first instance of open active contours (OAC) was already presented in the seminal work by Kass et al [1], and since then it has been adapted occasionally for applications that involve linear features, for example in geophysical [3], medical [12], or biological [4,13] contexts. Apart from distinct energy functionals that suit their respective applications, the different uses of OAC have been characterized by their different boundary conditions.…”

Section: Introductionmentioning

confidence: 99%

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Free Boundary Conditions Active Contours with Applications for Vision

Shemesh

Ben-Shahar

2011

Advances in Visual Computing

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Abstract. Active contours are used extensively in vision for more than two decades, primarily for applications such as image segmentation and object detection. The vast majority of active contours models make use of closed curves and the few that employ open curves rely on either fixed boundary conditions or no boundary conditions at all. In this paper we discuss a new class of open active contours with free boundary conditions, in which the end points of the open active curve are restricted to lie on two parametric boundary curves. We discuss how this class of curves may assist and facilitate various vision applications and we demonstrate its utility in applications such as boundary detection, feature tracking, and seam carving.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Free Boundary Conditions Active Contours with Applications for Vision

Shemesh

Ben-Shahar

2011

Advances in Visual Computing

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“…One method is based on appearance features [11], which assume that the shape of a certain cell or particle changes only slightly between consecutive frames. Another method is to maximize the smoothness of the particle trajectory and the velocity [10,12] for the trajectory direction and the velocity of a moving particle should only change slightly between consecutive frames. However, these methods do not work well with our data set as only partial information of the particles' movement is considered.…”

mentioning

confidence: 99%

“…Recently, several algorithms have been developed for tracking the motion of particles and cells [8][9][10][11][12]. One method is based on appearance features [11], which assume that the shape of a certain cell or particle changes only slightly between consecutive frames.…”

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confidence: 99%

Detection and tracking of clathrin-coated pits in biological images

Liu

Zhang

et al. 2012

Chin. Sci. Bull.

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Dynamically tracking hundreds of individual pits is essential to determine whether there exist "hot spots" for the formation of clathrin-coated pits or if the pits formed randomly on the plasma membrane. We propose an automated approach to detect these particles based on an improved á trous wavelet transform decomposition with automatic threshold selection and post processing solution, and to track the dynamic process with a greedy algorithm. The results indicate that the detection method can successfully detect most particles in an image with accuracy of 98.61% and 97.65% for adaptor and clathrin images, respectively, and that the tracking algorithm can resolve merging and splitting issues encountered when analyzing dynamic, live-cell images of clathrin assemblies. Recently, much attention has been focused on the development of segmentation and particle tracking techniques for the analysis of large volumes of microscopy fluorescence images acquired during cellular level cell imaging [1,2]. Particle tracking is one method of acquiring information about cellular dynamics. Clathrin-coated pits can be found in all nucleated cells, and provide an important means by which proteins and lipids are removed from the plasma membrane (endocytosis) and transported to an internal compartment (endosome) [3]. Using electronic microscopes, fluorescent-labeled versions of a variety of marker proteins have provided a tantalizing glimpse into the dynamics of the system in living cells. The clathrin pathway has thus acquired special status for analyzing molecular mechanisms in membrane traffic. A single image may capture hundreds or thousands of coated pits forming at the surface of a cell, all of which may not behave in the same way in time and space. Thus, information about these objects may need to be extracted and assembled at different time points and correlated with information from other images obtained at different spectral frequencies. For that purpose, clathrin light chains can be tagged with fluorescent proteins such as enhanced green fluorescent protein (EGFP) or yellow fluorescent protein (YFP) [1], and long time-series data can then be acquired using wide-field, confocal and total internal reflection fluorescence (TIRF) microscopy. Owing to the large quantity of images and hundreds of coated pits in each obtained image, an automated analysis approach is therefore necessary to visualize such molecular imaging data.The process for computerized analysis of cellular microscopy images generally consists of auto-segmentation, tracking and feature extraction. The detection of particles is the most critical step in molecular or sub-cellular image analysis where knowledge of the morphology of particles and the distribution of fluorescence signals in the particles is required. Although several particle detection methods based on segmentation have been developed [4,5], results have varied. Under-segmentation and/or over-segmentation can occur because these methods are based only on the intensity of the images or the morphologica...

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“…Model based tracking method (16) and shape based tracking method are also developed, but they cannot solve the correspondence problem. Recently, many algorithms have been developed trying to solve the correspondence problem in the motion of particles and cells (17)(18)(19)(20)(21). One method is based on appearance features (20), it assumes that the shape of a certain cell or particle only changes slightly between consecutive frames.…”

mentioning

confidence: 99%

Tracking molecular particles in live cells using fuzzy rule‐based system

et al. 2007

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Recent development of detection techniques of molecular particles in live cells has stimulated interest in developing the new powerful techniques to track the molecular particles in live cells. One special type of cellular microscopy images is about the formation and transportation of clathrin-coated pits and vesicles. Clathrin-coated pits are very important in studying the behavior of proteins and lipids in live cells. To answer the question, whether there exist ''hot spots'' for the formation of Clathrin-coated pits or the pits and arrays formed randomly on the plasma membrane, it is necessary to track many hundreds of individual pits dynamically in live-cell microscope movies to capture and monitor how pits and vesicles were formed. Therefore, a motion correspondence algorithm based on fuzzy rule-based system is proposed to resolve the problem of ambiguous association encountered in these dynamic, live-cell images of clathrin assemblies. Results show that this method can accurately track most of the particles in the high volume images. ' 2007 International Society for Analytical Cytology

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Automated Tracking and Modeling of Microtubule Dynamics

Cited by 30 publications

References 11 publications

Free Boundary Conditions Active Contours with Applications for Vision

Free Boundary Conditions Active Contours with Applications for Vision

Detection and tracking of clathrin-coated pits in biological images

Tracking molecular particles in live cells using fuzzy rule‐based system

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