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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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Bland and Altman plot of both measurements of number identified cells per area (Run1 and Run2).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.
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pone-0110289-g004: Bland and Altman plot of both measurements of number identified cells per area (Run1 and Run2).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.

Mentions: Variability between measurements was determined by the difference in mean nuclei density. The intraclass correlation coefficient (ICC) using two-way mixed single measures was 0.99. Bland and Altman plots to visualize the variability are depicted in figure 4. Outliers in this plot (differences of both measurements >0.50 or <0.50) are manually checked. This revealed that a discrepancy in masks caused these outliers. Figure 5 illustrates this difference; one mask contained air bubbles, and one mask contained a shadow artifact from the coverslip.


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)

Bland and Altman plot of both measurements of number identified cells per area (Run1 and Run2).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.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110289-g004: Bland and Altman plot of both measurements of number identified cells per area (Run1 and Run2).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.
Mentions: Variability between measurements was determined by the difference in mean nuclei density. The intraclass correlation coefficient (ICC) using two-way mixed single measures was 0.99. Bland and Altman plots to visualize the variability are depicted in figure 4. Outliers in this plot (differences of both measurements >0.50 or <0.50) are manually checked. This revealed that a discrepancy in masks caused these outliers. Figure 5 illustrates this difference; one mask contained air bubbles, and one mask contained a shadow artifact from the coverslip.

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