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SlideToolkit: an assistive toolset for the histological quantification of whole slide images.

Nelissen BG, van Herwaarden JA, Moll FL, van Diest PJ, Pasterkamp G - PLoS ONE (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Vascular Surgery, University Medical Center Utrecht, Utrecht, The Netherlands.

ABSTRACT
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.

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Related in: MedlinePlus

The four step slideToolkit workflow.An overview of the slideToolkit workflow with a summary and illustration for each step.
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pone-0110289-g001: The four step slideToolkit workflow.An overview of the slideToolkit workflow with a summary and illustration for each step.

Mentions: The slideToolkit is a collection of open-source scripts to handle each step from digital slide to the storage of your results. For a complete overview of all the tools, and there function in the slideToolkit, see table 1. An overview of the dependent libraries are listed in table 2. The toolkit is developed for modern (2014) personal computers (running *nix system [Linux, OS X, Unix]) and high-performance computing (HPC) systems. A common slideToolkit workflow consists of four consecutive steps. In the first step, “acquisition”, WSIs are collected and converted to TIFF files. In the second step, “preparation”, all the required files are created and organized. The third step, “tiles”, creates multiple manageable tiles to count. The fourth step, “analysis”, is the actual tissue analysis and saves the results in a meaningful data set. These steps are schematically depicted in figure 1. We present the slideToolkit as an open-source github repository (https://github.com/bglnelissen/slideToolkit).


SlideToolkit: an assistive toolset for the histological quantification of whole slide images.

Nelissen BG, van Herwaarden JA, Moll FL, van Diest PJ, Pasterkamp G - PLoS ONE (2014)

The four step slideToolkit workflow.An overview of the slideToolkit workflow with a summary and illustration for each step.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4220929&req=5

pone-0110289-g001: The four step slideToolkit workflow.An overview of the slideToolkit workflow with a summary and illustration for each step.
Mentions: The slideToolkit is a collection of open-source scripts to handle each step from digital slide to the storage of your results. For a complete overview of all the tools, and there function in the slideToolkit, see table 1. An overview of the dependent libraries are listed in table 2. The toolkit is developed for modern (2014) personal computers (running *nix system [Linux, OS X, Unix]) and high-performance computing (HPC) systems. A common slideToolkit workflow consists of four consecutive steps. In the first step, “acquisition”, WSIs are collected and converted to TIFF files. In the second step, “preparation”, all the required files are created and organized. The third step, “tiles”, creates multiple manageable tiles to count. The fourth step, “analysis”, is the actual tissue analysis and saves the results in a meaningful data set. These steps are schematically depicted in figure 1. We present the slideToolkit as an open-source github repository (https://github.com/bglnelissen/slideToolkit).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Vascular Surgery, University Medical Center Utrecht, Utrecht, The Netherlands.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus