3D segmentation of keratin intermediate filaments in confocal laser scanning microscopy

Herberich, Gerlind; Windoffer, Reinhard; Leube, Rudolf E.; Aach, Til

doi:10.1109/iembs.2011.6091910

Cited by 13 publications

(6 citation statements)

References 11 publications

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“…Locating ridges in a 2D image is a well-studied problem in mathematics and computer vision [ 17 ]; however, the 3D equivalent is a newer area of research [ 18 ]. Previous work also exists on the segmentation of cytoskeletons in fluorescence images [ 19 ] and the tracing of keratin intermediate filaments and neurons in confocal microscope images [ 20 , 21 , 22 ]. Depending on the nature of the filament network, various theoretical and heuristic approaches have been proposed [ 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia

et al. 2018

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Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin bundles, traditional approaches to filament detection and tracing have failed in these cases. In this study, we introduce BundleTrac, an effective method to trace hundreds of filaments in a bundle. A comparison between BundleTrac and manually tracing the actin filaments in a stereocilium showed that BundleTrac accurately built 326 of 330 filaments (98.8%), with an overall cross-distance of 1.3 voxels for the 330 filaments. BundleTrac is an effective semi-automatic modeling approach in which a seed point is provided for each filament and the rest of the filament is computationally identified. We also demonstrate the potential of a denoising method that uses a polynomial regression to address the resolution and high-noise anisotropic environment of the density map.

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Section: Introductionmentioning

confidence: 99%

Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia

et al. 2018

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“…We will refer to regions of assembly as Sources and regions of disassembly as Sinks. More details on the experimental data can be found in [10,21].…”

Section: Experimental Datamentioning

confidence: 99%

Keratin Dynamics: Modeling the Interplay between Turnover and Transport

et al. 2015

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Keratin are among the most abundant proteins in epithelial cells. Functions of the keratin network in cells are shaped by their dynamical organization. Using a collection of experimentally-driven mathematical models, different hypotheses for the turnover and transport of the keratin material in epithelial cells are tested. The interplay between turnover and transport and their effects on the keratin organization in cells are hence investigated by combining mathematical modeling and experimental data. Amongst the collection of mathematical models considered, a best model strongly supported by experimental data is identified. Fundamental to this approach is the fact that optimal parameter values associated with the best fit for each model are established. The best candidate among the best fits is characterized by the disassembly of the assembled keratin material in the perinuclear region and an active transport of the assembled keratin. Our study shows that an active transport of the assembled keratin is required to explain the experimentally observed keratin organization.

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“…Three‐dimensional (3D) segmentation methods of digital volumetric data (called z‐stacks) from confocal microscopy have been a research problem for a couple of decades (Lin et al ., ; McCullough et al ., ; Herberich et al ., ; Indhumathi et al ., ; Chen et al ., ). In its simplest form, 3D segmentation is about labelling each volumetric element (voxel) as foreground (FRG) or background.…”

Section: Introductionmentioning

confidence: 99%

A method for the evaluation of thousands of automated 3D stem cell segmentations

Bajcsy

Simon

Florczyk

et al. 2015

Journal of Microscopy

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There is no segmentation method that performs perfectly with any data set in comparison to human segmentation. Evaluation procedures for segmentation algorithms become critical for their selection. The problems associated with segmentation performance evaluations and visual verification of segmentation results are exaggerated when dealing with thousands of 3D image volumes because of the amount of computation and manual inputs needed. We address the problem of evaluating 3D segmentation performance when segmentation is applied to thousands of confocal microscopy images (z-stacks). Our approach is to incorporate experimental imaging and geometrical criteria, and map them into computationally efficient segmentation algorithms that can be applied to a very large number of z-stacks. This is an alternative approach to considering existing segmentation methods and evaluating most state-of-the-art algorithms. We designed a methodology for 3D segmentation performance characterization that consists of design, evaluation and verification steps. The characterization integrates manual inputs from projected surrogate “ground truth” of statistically representative samples and from visual inspection into the evaluation. The novelty of the methodology lies in (1) designing candidate segmentation algorithms by mapping imaging and geometrical criteria into algorithmic steps, and constructing plausible segmentation algorithms with respect to the order of algorithmic steps and their parameters, (2) evaluating segmentation accuracy using samples drawn from probability distribution estimates of candidate segmentations, and (3) minimizing human labor needed to create surrogate “truth” by approximating z-stack segmentations with 2D contours from three orthogonal z-stack projections and by developing visual verification tools. We demonstrate the methodology by applying it to a dataset of 1253 mesenchymal stem cells. The cells reside on 10 different types of biomaterial scaffolds, and are stained for actin and nucleus yielding 128 460 image frames (on average 125 cells/scaffold × 10 scaffold types × 2 stains × 51 frames/cell). After constructing and evaluating six candidates of 3D segmentation algorithms, the most accurate 3D segmentation algorithm achieved an average precision of 0.82 and an accuracy of 0.84 as measured by the Dice similarity index where values greater than 0.7 indicate a good spatial overlap. A probability of segmentation success was 0.85 based on visual verification, and a computation time was 42.3 h to process all z-stacks. While the most accurate segmentation technique was 4.2 times slower than the second most accurate algorithm, it consumed on average 9.65 times less memory per z-stack segmentation.

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3D segmentation of keratin intermediate filaments in confocal laser scanning microscopy

Cited by 13 publications

References 11 publications

Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia

Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia

Keratin Dynamics: Modeling the Interplay between Turnover and Transport

A method for the evaluation of thousands of automated 3D stem cell segmentations

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