Quantifying Actin Filaments in Microscopic Images using Keypoint Detection Techniques and A Fast Marching Algorithm

Liu, Yi; Nedo, Alexander; Seward, Kody; Caplan, Jeffrey; Kambhamettu, Chandra

doi:10.1109/icip40778.2020.9191337

Cited by 7 publications

(8 citation statements)

References 15 publications

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“…Importantly, we found that ILEE and the ILEE_CSK library can process both plant and animal images with a satisfying performance. This is encouraging as ILEE can substitute or improve the Hough transform, a straight-line detection algorithm commonly used for animal cytoskeleton (generally straight and thick), but with some limitations in neglecting and miscalculating curvy cytoskeleton fractions ( Liu et al, 2020 ; Liu et al, 2018 ). With the advancement of ILEE, Hough transform-based analysis may not be essential, and the potential cytoskeleton indices that rigorously require the Hough transform can still utilize ILEE as a provider of binary image input for more accurate results.…”

Section: Resultsmentioning

confidence: 90%

“…Lastly, the performance of existing algorithms varies significantly depending on the sample source. This hurdle imposes a considerable disparity in the algorithm performance for plants—which possess a dominance of curvy and spherically distributed filaments—compared to the animal cytoskeletal organization, which is generally straight and complanate ( Liu et al, 2020 , 2018 ; Alioscha-Perez et al, 2016 ). In fact, while the sample source dramatically impacts the ability to evaluate features of cytoskeletal function across all eukaryotes, most current approaches are developed based on cytoskeletal images from animal cells, indicating potential systemic bias when applied to other types of image samples, such as plants.…”

Section: Introductionmentioning

confidence: 99%

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ILEE: Algorithms and toolbox for unguided and accurate quantitative analysis of cytoskeletal images

Zhang

Tong

et al. 2022

Journal of Cell Biology

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The eukaryotic cytoskeleton plays essential roles in cell signaling and trafficking, broadly associated with immunity and diseases in humans and plants. To date, most studies describing cytoskeleton dynamics and function rely on qualitative/quantitative analyses of cytoskeletal images. While state-of-the-art, these approaches face general challenges: the diversity among filaments causes considerable inaccuracy, and the widely adopted image projection leads to bias and information loss. To solve these issues, we developed the Implicit Laplacian of Enhanced Edge (ILEE), an unguided, high-performance approach for 2D/3D-based quantification of cytoskeletal status and organization. Using ILEE, we constructed a Python library to enable automated cytoskeletal image analysis, providing biologically interpretable indices measuring the density, bundling, segmentation, branching, and directionality of the cytoskeleton. Our data demonstrated that ILEE resolves the defects of traditional approaches, enables the detection of novel cytoskeletal features, and yields data with superior accuracy, stability, and robustness. The ILEE toolbox is available for public use through PyPI and Google Colab.

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Section: Resultsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

ILEE: Algorithms and toolbox for unguided and accurate quantitative analysis of cytoskeletal images

Zhang

Tong

et al. 2022

Journal of Cell Biology

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show abstract

“…Importantly, we found that ILEE and ILEE_CSK library can process both plant and animal images with satisfying performance. This is encouraging, as ILEE can substitute or improve Hough transform, a straight-line detection algorithm commonly used for animal cytoskeleton (generally straight and thick), but with some limitation in neglecting and miscalculating curvy cytoskeleton fractions 14,15 . With the advancement of ILEE, Hough transform-based analysis may not be essential, and the potential cytoskeleton indices that rigorously require Hough transform can still utilize ILEE as a provider of binary image input for more accurate results.…”

Section: Ilee Has Broad Compatibility With Various Sample Typesmentioning

confidence: 91%

“…Lastly, the performance of existing algorithms varies significantly depending on the sample source. This hurdle imposes a considerable disparity in the algorithm performance for plants -which possess a dominance of curvy and fluctuating filaments -compared to the animal cytoskeletal organization, which is generally straight and complanate [14][15][16] . In fact, while sample source dramatically impacts our ability to evaluate the features of cytoskeletal function across all eukaryotes, the vast majority of current approaches are developed based on cytoskeletal images from animal cells, which indicates potential systemic bias when applied to other types of image samples, such as plants.…”

Section: Introductionmentioning

confidence: 99%

Implicit Laplacian of Enhanced Edge: An Unguided Algorithm for Accurate and Automated Quantitative Analysis of Cytoskeletal Images

Zhang

Day

et al. 2021

Preprint

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The eukaryotic cytoskeleton plays essential roles in cell signaling, trafficking, and motion. Recent work towards defining the temporal and spatial dynamics of cytoskeletal organization, including as a function of cell status, has utilized quantitative analysis of cytoskeletal fluorescence images as a standard approach to define cytoskeletal function. However, due to the uneven spatial distribution of the cytoskeleton, including varied shape and unstable binding efficiency to staining markers, these approaches may not segment cytoskeletal fractions accurately. Additionally, quantitative approaches currently suffer from human bias as well as information loss caused by z-axis projection of raw images. To overcome these obstacles, we developed Implicit Laplacian of Enhanced Edge (ILEE), a cytoskeletal component segmentation algorithm, which uses an 2D/3D-compatible, unguided local thresholding approach, therefore providing less biased and stable results. Empowered by ILEE, we constructed a Python based library for automated quantitative analysis of cytoskeleton images, which computes cytoskeletal indices that covers density, bundling, severing, branching, and directionality. Comparing to various classic approaches, ILEE library generates descriptive data with higher accuracy, robustness, and efficiency. In addition to the analysis described herein, we have developed an open-access ILEE library for community use.

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“…Liu et al [49] propose a DL-based method for geometrical and topological characterisation of actin filament networks. For an initial binary segmentation, they use the U-net based method by Liu et al [48] .…”

Section: Deep Learning Methods For Enhancement and Segmentation Of Cytoskeletonsmentioning

confidence: 99%

Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review

Özdemir

Reski

2021

Computational and Structural Biotechnology Journal

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Cytoskeletal filaments are structures of utmost importance to biological cells and organisms due to their versatility and the significant functions they perform. These biopolymers are most often organised into network-like scaffolds with a complex morphology. Understanding the geometrical and topological organisation of these networks provides key insights into their functional roles. However, this non-trivial task requires a combination of high-resolution microscopy and sophisticated image processing/analysis software. The correct analysis of the network structure and connectivity needs precise segmentation of microscopic images. While segmentation of filament-like objects is a well-studied concept in biomedical imaging, where tracing of neurons and blood vessels is routine, there are comparatively fewer studies focusing on the segmentation of cytoskeletal filaments and networks from microscopic images. The developments in the fields of microscopy, computer vision and deep learning, however, began to facilitate the task, as reflected by an increase in the recent literature on the topic. Here, we aim to provide a short summary of the research on the (semi-)automated enhancement, segmentation and tracing methods that are particularly designed and developed for microscopic images of cytoskeletal networks. In addition to providing an overview of the conventional methods, we cover the recently introduced, deep-learning-assisted methods alongside the advantages they offer over classical methods.

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Quantifying Actin Filaments in Microscopic Images using Keypoint Detection Techniques and A Fast Marching Algorithm

Cited by 7 publications

References 15 publications

ILEE: Algorithms and toolbox for unguided and accurate quantitative analysis of cytoskeletal images

ILEE: Algorithms and toolbox for unguided and accurate quantitative analysis of cytoskeletal images

Implicit Laplacian of Enhanced Edge: An Unguided Algorithm for Accurate and Automated Quantitative Analysis of Cytoskeletal Images

Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review

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