Actin Filament Segmentation Using Spatiotemporal Active-Surface and Active-Contour Models

Li, Hongsheng; Shen, Tian; Vavylonis, Dimitrios; Huang, Xiaolei

doi:10.1007/978-3-642-15705-9_11

Cited by 16 publications

(19 citation statements)

References 10 publications

(20 reference statements)

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“…Active contour based methods increase segmentation robustness by incorporating prior information about the object shape. Unlike the previous image processing or template matching based methods, open curve parametric active contours (Kass et al, 1988; Berger and Mohr, 1990) explicitly model the linear nature of filaments, and have been successfully applied to the segmentation of actin filaments (Li et al, 2009a,b, 2010; Smith et al, 2010), microtubules (Kong et al, 2005; El-Saban and Manjunath, 2005; El-Saban et al, 2006; Altinok et al, 2006; Nurgaliev et al, 2010), and axons (Wang et al, 2011). These previous methods mostly segment individual filaments instead of extracting both geometry and topology of the entire curvilinear network.…”

Section: Introductionmentioning

confidence: 99%

3D actin network centerline extraction with multiple active contours

Vavylonis

Huang

2014

Medical Image Analysis

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Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and actin cables. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we propose a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D Total Internal Reflection Fluorescence Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy. Quantitative evaluation of the method using synthetic images shows that for images with SNR above 5.0, the average vertex error measured by the distance between our result and ground truth is 1 voxel, and the average Hausdorff distance is below 10 voxels.

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Section: Introductionmentioning

confidence: 99%

3D actin network centerline extraction with multiple active contours

Vavylonis

Huang

2014

Medical Image Analysis

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“…Li et al [7, 8, 9] used Stretching Open Active Contours (SOACs) to segment and track individual filaments in a TIRFM image sequence. Smith et al [10] further applied this method to quantify the conformations and dynamics of actin cables in 3D confocal microscopy images.…”

Section: Introductionmentioning

confidence: 99%

Extraction and analysis of actin networks based on Open Active Contour models

Shen

et al. 2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Network structures formed by actin filaments are present in many kinds of fluorescence microscopy images. In order to quantify the conformations and dynamics of such actin filaments, we propose a fully automated method to extract actin networks from images and analyze network topology. The method handles well intersecting filaments and, to some extent, overlapping filaments. First we automatically initialize a large number of Stretching Open Active Contours (SOACs) from ridge points detected by searching for plus-to-minus sign changes in the gradient map of the image. These initial SOACs then elongate simultaneously along the bright center-lines of filaments by minimizing an energy function. During their evolution, they may merge or stop growing, thus forming a network that represents the topology of the filament ensemble. We further detect junction points in the network and break the SOACs at junctions to obtain "SOAC segments". These segments are then re-grouped using a graph-cut spectral clustering method to represent the configuration of actin filaments. The proposed approach is generally applicable to extracting intersecting curvilinear structures in noisy images. We demonstrate its potential using two kinds of data: (1) actin filaments imaged by Total Internal Reflection Fluorescence Microscopy (TIRFM) in vitro; (2) actin cytoskeleton networks in fission yeast imaged by spinning disk confocal microscopy.

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“…The above methods only consider temporal information up to the current frame and thus ignore all available information after it. In [10], a 2D time-lapse image sequence is treated as a 3D image volume, and a spatiotemporal active surface model is proposed to segment an actin filament in all frames simultaneously. The assumption of this method is that an actin filament's body remains static across time.…”

Section: Introductionmentioning

confidence: 99%

“…Dynamic programming has been used to optimize tree models in [11] or chain models in [12]. Compared with existing methods [9],[10], which have some strong assumptions and constraints, our proposed method is more flexible. Every constraint in the framework can be freely modified or deleted, and new constraints can be easily added in.…”

Section: Introductionmentioning

confidence: 99%

Actin Filament Segmentation Using Dynamic Programming

Shen

Huang

2011

Lecture Notes in Computer Science

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We introduce a novel algorithm for actin filament segmentation in 2D TIRFM image sequences. This problem is difficult because actin filaments dynamically change shapes during their growth, and the TIRFM images are usually noisy. We ask a user to specify the two tips of a filament of interest in the first frame. We then model the segmentation problem in an image sequence as a temporal chain, where its states are tip locations; given candidate tip locations, actin filaments' body points are inferred by a dynamic programming method, which adaptively generates candidate solutions. Combining candidate tip locations and their inferred body points, the temporal chain model is efficiently optimized using another dynamic programming method. Evaluation on noisy TIRFM image sequences demonstrates the accuracy and robustness of this approach.

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Actin Filament Segmentation Using Spatiotemporal Active-Surface and Active-Contour Models

Cited by 16 publications

References 10 publications

3D actin network centerline extraction with multiple active contours

3D actin network centerline extraction with multiple active contours

Extraction and analysis of actin networks based on Open Active Contour models

Actin Filament Segmentation Using Dynamic Programming

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