SlideToolkit: An Assistive Toolset for the Histological Quantification of Whole Slide Images

<div><p>The demand for accurate and reproducible phenotyping of a disease trait increases with the rising number of biobanks and genome wide association studies. Detailed analysis of histology is a powerful way of phenotyping human tissues. Nonetheless, purely visual assessment of histological slides is time-consuming and liable to sampling variation and optical illusions and thereby observer variation, and external validation may be cumbersome. Therefore, within our own biobank, computerized quantification of digitized histological slides is often preferred as a more precise and reproducible, and sometimes more sensitive approach. Relatively few free toolkits are, however, available for fully digitized microscopic slides, usually known as whole slides images. In order to comply with this need, we developed the slideToolkit as a fast method to handle large quantities of low contrast whole slides images using advanced cell detecting algorithms. The slideToolkit has been developed for modern personal computers and high-performance clusters (HPCs) and is available as an open-source project on github.com. We here illustrate the power of slideToolkit by a repeated measurement of 303 digital slides containing CD3 stained (DAB) abdominal aortic aneurysm tissue from a tissue biobank. Our workflow consists of four consecutive steps. In the first step (acquisition), whole slide images are collected and converted to TIFF files. In the second step (preparation), files are organized. The third step (tiles), creates multiple manageable tiles to count. In the fourth step (analysis), tissue is analyzed and results are stored in a data set. Using this method, two consecutive measurements of 303 slides showed an intraclass correlation of 0.99. In conclusion, slideToolkit provides a free, powerful and versatile collection of tools for automated feature analysis of whole slide images to create reproducible and meaningful phenotypic data sets.</p></div

Bland and Altman plot of both measurements of number identified cells per area (Run1 and Run2).

<p>Measurements were log transformed, log(0.0001+ identified cells per area). The intraclass correlation coefficient (ICC) using two-way mixed single measures was 0.99.</p

Tools available in the slideToolkit.

<p><b>For each tool a accompanying help text can be found by running the tool followed by the —help flag (e.g. slideMask –help).</b></p><p>Different tools are used in different steps. Most tools depend on other libraries and software packages.</p><p>Tools available in the slideToolkit.</p

An example of object identification using the pipeline.

<p>a. and c. are original tiles, b. and d. are the same tiles with oulines of the identified objects. Blue lines outline the areas identified as tissue, green lines outline areas identified as DAB positive nuclei. These images are created using the 'SaveImages' module in CellProfiler.</p

A visualisation of a multi-page pyramid TIFF file.

<p>This illustration shows a TIFF file with 4 layers (thumbnail, 1.25x, 20x, 40x), digital slides stored as TIFF files often contain up to 11 or more layers.</p

An example of discrepancy in mask formation.

<p>An air bubble was not masked in a., this same bubble was masked in b. The shadow of the coverslip (on the left side of the image) was not masked in c., this same shadow was masked in d. Automatically created masks should be checked manually to avoid unexpected results.</p

SlideToolkit software dependencies, licenses and project websites.

<p><b>a:</b><a href="http://www.gnu.org/licenses/" target="_blank">www.gnu.org/licenses/</a></p><p><b>b:</b><a href="http://www.remotesensing.org/libtiff/" target="_blank">www.remotesensing.org/libtiff/</a></p><p><b>c:</b><a href="http://www.imagemagick.org/script/license.php" target="_blank">www.imagemagick.org/script/license.php</a></p><p>Copies of the different licenses can be found on the associated websites.</p><p>SlideToolkit software dependencies, licenses and project websites.</p

Digital slide file characteristics from Run 1.

<p>1 MB  = 1024*1024 bytes.</p><p>Digital slide file characteristics from Run 1.</p

Tools used, wall-clock times and instructions.

<p><b>Wall-clock times are measured using GNU-time.</b></p><p><b>a: wall-clock time is calculated in total.</b></p><p><b>B: wall-clock time is calculated per digital slide (median, 1st–3rd quartile).</b></p><p><b>Mac mini: 2.0 GHz i7, 16 GB RAM (capable of 8 threads).</b></p><p><b>HPC: (8x Intel(R) Xeon(R) CPU E5-2630 0 @ 2.30 GHz, 38x Intel(R) Xeon(R) CPU E5-2640 0 @ 2.50 GHz, 11x Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60 GHz, all with 12 cores and 128 GB RAM per node), ‘-pe threaded 3’ represents three threads and 45 GB RAM (for each thread an additional 15 GB becomes available).</b></p><p>One sample failed in the 'Tiles' step. Total elapsed wall-clock times are obtained using the GNU-time utility.</p><p>Tools used, wall-clock times and instructions.</p

The four step slideToolkit workflow.

<p>An overview of the slideToolkit workflow with a summary and illustration for each step.</p