Limits...
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.


Simulated test case image featuring many parallel lines at different distances, some very close.It also contains several crossings of lines of different brightness at small angles. The image is used as a test case to qualitatively highlight the performance of the three methods compared here in terms of filament pixel detection and especially structure detection. As the FS is the only method that extracts line data, the notion of parallel or intersecting lines only makes sense for this method.
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pone.0126346.g011: Simulated test case image featuring many parallel lines at different distances, some very close.It also contains several crossings of lines of different brightness at small angles. The image is used as a test case to qualitatively highlight the performance of the three methods compared here in terms of filament pixel detection and especially structure detection. As the FS is the only method that extracts line data, the notion of parallel or intersecting lines only makes sense for this method.

Mentions: To illustrate the limits of the methods compared qualitatively, in view of the challenges outlined as IIa) to IIc) from Section “The Filament Sensor and the Benchmark Dataset”, we picked three suitable examples from the benchmark dataset and the specifically simulated image, Fig 11. In this context, “segmentation” means the detection of lines or line pixels. Therefore we refer to the detection of excess lines or line pixels as oversegmentation.


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)

Simulated test case image featuring many parallel lines at different distances, some very close.It also contains several crossings of lines of different brightness at small angles. The image is used as a test case to qualitatively highlight the performance of the three methods compared here in terms of filament pixel detection and especially structure detection. As the FS is the only method that extracts line data, the notion of parallel or intersecting lines only makes sense for this method.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0126346.g011: Simulated test case image featuring many parallel lines at different distances, some very close.It also contains several crossings of lines of different brightness at small angles. The image is used as a test case to qualitatively highlight the performance of the three methods compared here in terms of filament pixel detection and especially structure detection. As the FS is the only method that extracts line data, the notion of parallel or intersecting lines only makes sense for this method.
Mentions: To illustrate the limits of the methods compared qualitatively, in view of the challenges outlined as IIa) to IIc) from Section “The Filament Sensor and the Benchmark Dataset”, we picked three suitable examples from the benchmark dataset and the specifically simulated image, Fig 11. In this context, “segmentation” means the detection of lines or line pixels. Therefore we refer to the detection of excess lines or line pixels as oversegmentation.

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.