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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.


Performance comparison for inhomogeneous brightness and crossing lines.Showing a detail of cell M3. The subfigures represent (a) the original detail, (b) the results of the FS, (c) the results of the eLoG method and (d) the results of CID. Green pixels are false positives detected by the method, yellow are correctly identified pixels and red are missed pixels as in Fig 9.The FS produces a fair amount of false positives but fares quite well both in the dark region on the left as well as the bright region with crossing lines on the right. The eLoG method also find parts of the lines in the dark region albeit at the expense of significant oversegmentation in the bright region. CID detects lines almost exclusively in the higher contrast bright region, where it produces a cobweb structure with an amount of oversegmentation similar to the FS.
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pone.0126346.g012: Performance comparison for inhomogeneous brightness and crossing lines.Showing a detail of cell M3. The subfigures represent (a) the original detail, (b) the results of the FS, (c) the results of the eLoG method and (d) the results of CID. Green pixels are false positives detected by the method, yellow are correctly identified pixels and red are missed pixels as in Fig 9.The FS produces a fair amount of false positives but fares quite well both in the dark region on the left as well as the bright region with crossing lines on the right. The eLoG method also find parts of the lines in the dark region albeit at the expense of significant oversegmentation in the bright region. CID detects lines almost exclusively in the higher contrast bright region, where it produces a cobweb structure with an amount of oversegmentation similar to the FS.

Mentions: Comparison for inhomogeneous brightness and crossing of lines is illustrated in Fig 12 which shows a detail from cell image M3. The upper right image regions display crossings of filaments which are almost completely captured by the FS and slightly less by CID, that tends to produce a network structure with only short straight segments. The eLoG method highly oversegments, essentially identifying all pixels in this region. Oversegmentation is also done by the FS and CID, but on a much lower scale. Image inhomogeneity is introduced by the dark area on the left. Clearly, the FS is the only method that finds most of the labeled segments, followed by the eLoG method that tends to break longer lines into pieces. While CID finds almost none of the filaments, together with the eLoG method it also features almost no oversegmentation in this area. In contrast, the oversegmentation by the FS features line segments that are visible in the raw image also, that were not labeled by the human expert.


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)

Performance comparison for inhomogeneous brightness and crossing lines.Showing a detail of cell M3. The subfigures represent (a) the original detail, (b) the results of the FS, (c) the results of the eLoG method and (d) the results of CID. Green pixels are false positives detected by the method, yellow are correctly identified pixels and red are missed pixels as in Fig 9.The FS produces a fair amount of false positives but fares quite well both in the dark region on the left as well as the bright region with crossing lines on the right. The eLoG method also find parts of the lines in the dark region albeit at the expense of significant oversegmentation in the bright region. CID detects lines almost exclusively in the higher contrast bright region, where it produces a cobweb structure with an amount of oversegmentation similar to the FS.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0126346.g012: Performance comparison for inhomogeneous brightness and crossing lines.Showing a detail of cell M3. The subfigures represent (a) the original detail, (b) the results of the FS, (c) the results of the eLoG method and (d) the results of CID. Green pixels are false positives detected by the method, yellow are correctly identified pixels and red are missed pixels as in Fig 9.The FS produces a fair amount of false positives but fares quite well both in the dark region on the left as well as the bright region with crossing lines on the right. The eLoG method also find parts of the lines in the dark region albeit at the expense of significant oversegmentation in the bright region. CID detects lines almost exclusively in the higher contrast bright region, where it produces a cobweb structure with an amount of oversegmentation similar to the FS.
Mentions: Comparison for inhomogeneous brightness and crossing of lines is illustrated in Fig 12 which shows a detail from cell image M3. The upper right image regions display crossings of filaments which are almost completely captured by the FS and slightly less by CID, that tends to produce a network structure with only short straight segments. The eLoG method highly oversegments, essentially identifying all pixels in this region. Oversegmentation is also done by the FS and CID, but on a much lower scale. Image inhomogeneity is introduced by the dark area on the left. Clearly, the FS is the only method that finds most of the labeled segments, followed by the eLoG method that tends to break longer lines into pieces. While CID finds almost none of the filaments, together with the eLoG method it also features almost no oversegmentation in this area. In contrast, the oversegmentation by the FS features line segments that are visible in the raw image also, that were not labeled by the human expert.

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.