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The filament sensor for near real-time detection of cytoskeletal fiber structures.

Eltzner B, Wollnik C, Gottschlich C, Huckemann S, Rehfeldt F - PLoS ONE (2015)

Bottom Line: Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images.The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy.The implementation of the FS and the benchmark database are available as open source.

View Article: PubMed Central - PubMed

Affiliation: Institute for Mathematical Stochastics, Georg-August-University, 37077 Göttingen, Germany.

ABSTRACT
A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length, and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source.

No MeSH data available.


Comparison of Rfp (false positive ratios) and Rfn (false negative ratios).Subfigures are as in 15, now for simulated cells, with all axes linear. Data points corresponding to same methods in plots (a) and (c) are connected only for better visualization.
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pone.0126346.g021: Comparison of Rfp (false positive ratios) and Rfn (false negative ratios).Subfigures are as in 15, now for simulated cells, with all axes linear. Data points corresponding to same methods in plots (a) and (c) are connected only for better visualization.

Mentions: Let Nt denote the number of ground truth pixels for which at least one pixel identified by the method is in a 3 × 3-square around it, Nfn the number of other ground truth pixels and Nfp the number of pixels detected by the method for which no ground truth pixel is in a 3 × 3-square around it. We define the false negative ratio as Rfn = Nfn/N where N = Nt + Nfn and the false positive ratio as Rfp = Nfp/N. The results are displayed in Figs 15 and 21.


The filament sensor for near real-time detection of cytoskeletal fiber structures.

Eltzner B, Wollnik C, Gottschlich C, Huckemann S, Rehfeldt F - PLoS ONE (2015)

Comparison of Rfp (false positive ratios) and Rfn (false negative ratios).Subfigures are as in 15, now for simulated cells, with all axes linear. Data points corresponding to same methods in plots (a) and (c) are connected only for better visualization.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4440737&req=5

pone.0126346.g021: Comparison of Rfp (false positive ratios) and Rfn (false negative ratios).Subfigures are as in 15, now for simulated cells, with all axes linear. Data points corresponding to same methods in plots (a) and (c) are connected only for better visualization.
Mentions: Let Nt denote the number of ground truth pixels for which at least one pixel identified by the method is in a 3 × 3-square around it, Nfn the number of other ground truth pixels and Nfp the number of pixels detected by the method for which no ground truth pixel is in a 3 × 3-square around it. We define the false negative ratio as Rfn = Nfn/N where N = Nt + Nfn and the false positive ratio as Rfp = Nfp/N. The results are displayed in Figs 15 and 21.

Bottom Line: Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images.The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy.The implementation of the FS and the benchmark database are available as open source.

View Article: PubMed Central - PubMed

Affiliation: Institute for Mathematical Stochastics, Georg-August-University, 37077 Göttingen, Germany.

ABSTRACT
A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length, and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source.

No MeSH data available.