Data collected by various methods and comparison of runtime.

<p>Checkmarks in parentheses indicate that for lack of documentation and source code availability the FibreScore method is not portable in terms of full configurability and usability to a generic system.</p><p>Data collected by various methods and comparison of runtime.</p

Tracing results with false positives and missed filaments for an hMSC.

<p>The cell G1 used for this illustration is a fixed cell that has been immunofluorescently stained. The subfigures display: (a) ground truth, (b) FS results, (c) eLoG results, (d) CID results. Green pixels are false positives detected by the method, yellow are correctly identified pixels and red are missed pixels. A pixel is correctly identified, if it corresponds to a ground truth labeled pixel within an 8-neighborhood. A ground truth labeled pixel is considered missing, if no pixel was detected within an 8-neighborhood.</p

Comparison of <i>R</i>_fp (false positive ratios) and <i>R</i>_fn (false negative ratios).

<p>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.</p

Illustration of the line Gaussian.

<p>(a) an isotropic two dimensional Gaussian. Convolving the image with such a filter will lead to blurring of the image. (b) restriction of an isotropic Gaussian to a line. This filter locally homogenizes pixel brightness along lines that run in direction of the filter. It is computationally efficient as it only uses few pixels. (c) an elongated Gaussian. This filter also homogenizes lines, but it has much more Pixels and thus requires much longer computation.</p

Flow chart illustrating the algorithm of the line segment sensor.

<p>The algorithm begins at the very top with a binary image and outputs an orientation field and line information, displayed to the bottom left of the chart. A detailed explanation is given in the text.</p

Performance comparison for inhomogeneous brightness and crossing lines.

<div><p>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 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126346#pone.0126346.g009" target="_blank">Fig 9</a>.</p> <p>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.</p></div

Comparison of angular histograms.

<p>Subfigures are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126346#pone.0126346.g014" target="_blank">Fig 14</a>, now for simulated cells, with all relative masses and distances now linear. Data points corresponding to same methods in plots (a) and (c) are connected only for better visualization.</p

Angular histograms of filament mass for an hMSC.

<p>The cell G1 used for this illustration is a fixed cell that has been immunofluorescently stained. The subfigures display: (a) ground truth, (b) FS results, (c) eLoG results, (d) Hough results. The black curves illustrate the result of kernel smoothing with a Gaussian of standard deviation <i>σ</i> = 10.</p

Comparison of <i>R</i>_fp (false positive ratios) and <i>R</i>_fn (false negative ratios).

<p>Subfigure (a) displays <i>R</i>_fp for individual cells, subfigure (b) the corresponding boxplots over all 10 cells. Subfigure (c) displays <i>R</i>_fp for individual cells, subfigure (d) shows the corresponding boxplots over all 10 cells. The plots are semi-logarithmic because scales vary widely. Data points corresponding to same methods in plots (a) and (c) are connected only for better visualization.</p

Filament quality scores of benchmark database images.

<p>FQS is determined in terms of line sharpness and contrast, size of the cell, and bright spots due to overexposure, where the latter decreases the score, while the line sharpness and contrast and cell size contribute positively. The blue lines indicate our separation of the images into qualitative classes. The classes were chosen to contain like numbers of images and only serve illustrative purposes as a rough quality label.</p