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Stereology Toolkit for ImageScope of Aperio


ADCIS, in partnership with Aperio® Technologies, Inc. (Leica) and F. Baclesse Cancer Centre, announces a powerful stereology software extension to use with version 12.2.2.5015 or previous versions of Aperio's ImageScope® slide viewing software.

To use stereology tools without ImageScope, refer to the Stereology Analyzer software product.

Stereology Toolkit GUI Stereology Toolkit GUI

The Stereology Toolkit for ImageScope implements long-standing and accepted stereology techniques that utilize a test grid overlaid on a slide image to characterize its microscopic structures. This extension's techniques can be easily applied to virtual slides acquired using an Aperio ScanScope® scanning system in order to compute accurate estimates of biomarker parameters, including volume fraction and length density.

The Stereology Toolkit for ImageScope enables the user to define a region of interest (ROI) and a grid that overlay an area of a virtual slide. The type and spacing of the grid can be adjusted by the user, as shown beside, to achieve the best estimate of biomarker parameters in the ROI. After the ROI and grid are defined, the user manually highlights structures of interest (i.e., biomarkers) that are intersected by the test grid. The number of points is then used to automatically compute biomarker parameters using stereological statistical analyses. The extension's results can be exported into Aperio's Spectrum® environment for display and further analysis of the prognostic and therapeutic impact of biomarkers.

3D objects cut by 2D slice The term stereology was first introduced in 1961 when the International Society for Stereology (ISS) was founded by a small group of scientists, although the basis of stereology theory was defined more than 200 years ago. By definition, stereology is the science that studies the geometrical relationship between a structure that exists in 3D space and a set of images of the same structure that are fundamentally defined in 2D space (images of sections or projections). Note that standard 2D image processing techniques will hardly provide 3D information from sections, except for the volume fraction value.

Techniques to Validate and Compare Image Processing Algorithms

Figure 4

Pathologists and histologists can use the Stereology Toolkit's techniques to quickly compute unbiased and robust estimates of biomarker estimates on virtual slides. It is an effective alternative to complex image processing algorithms. In addition, while complex algorithms require careful validation of their results, stereology results require no validation since they are derived from a standard statistical analysis of manually counted points. When the structures of interest cannot be highlighted by specific staining (e.g., histochemical or immunohistochemical stainings), or when staining is not optimal, then the techniques of stereology are the best alternative for estimating the parameters of biomarkers.

Figure 5

When specific staining is effective, different image processing and analysis algorithms can be developed that vary in their complexity and efficiency. The Stereology Toolkit for ImageScope is a powerful tool for selecting the algorithm that best achieves accurate estimates of biomarker parameters. The regular sampling process used in stereology science helps overcome the difficult problem resulting from tissue heterogeneity (see adjacent figure). The special strength of the Stereology Toolkit is that it always provides unbiased estimates of biomarker parameters for any complexity of sample tissue. When combining stereology and image processing, the user has a broad set of powerful tools to characterize biomarkers on virtual slides.


Specifications

The Stereology Toolkit for ImageScope has the following capabilities:

  • Grid
User definable, point counting grid and grid of uniformly sampled squares with forbidden lines.
  • Region of interest
User definable, multiple regions allowed, can be any regular shape (e.g., ellipse, rectangle) or free-hand drawn.
  • Undesired tissue
User can exclude undesired tissue regions.
  • Input
Images supported by Aperio ImageScope (TIFF, JPEG).
  • Output
Volume fraction (point counting frame), numerical density of biomarker profiles per unit area (frames with forbidden lines).
Region of Interest

Figure 1: Region of Interest (ROI)
Define a region-of-interest consisting of one or multiple regions using rectangle, ellipse or freehand drawing tools.

Grid

Figure 2: Grid
Select the grid type, choosing from point counting and uniformly sampled squares with forbidden lines, and define the spacing.

Manual Classification

Figure 3: Manual Classification
Highlight structures of interest (e.g. biomarkers) on the grid.

Stereology Results

Figure 4: Stereology Results
The system automatically computes biomarker parameters such as volume fraction or numerical density of biomarker profiles per unit area based on stereological statistical analysis.



Stereology Toolkit for ImageScope main benefits:

  • Perform measurements when image processing is not possible or too complex
  • Best method to validate image processing algorithms on virtual slides
  • Provides unbiased results
  • Estimates 3D characteristics of structures from 2D sections
  • Fully integrated in the Aperio ImageScope environment


Additional unique capabilities:

With the Stereology Toolkit for ImageScope, it is also possible to appraise the sensitivity and specificity of any image processing algorithm. Comparing the results of stereology and an image processing algorithm can be done by determining whether the image processing results fall in the range of the stereology results. Furthermore, the user can estimate the rate of false positive and false negative events to calculate the sensitivity and specificity of any automatic image processing algorithm. Future plans call for the development of a fully automatic comparison between the two results. Also planned is the addition of other test probes, including implementation of second order stereology tools combined with improved precision measurements.


References:

  • G. Matheron, 1975. Random set and integral geometry. J. Wiley and sons, New York, USA.
  • E.R. Weibel, 1981. Stereological methods in cell biology: where are we? where are we going? J. Histochem. Cytochem., 29, 1043-1052.
  • H. J. G. Gundersen, R. Osterby, 1981. Optimizing sampling efficiency of stereological studies in biology: or "Do more less well!". J. Microsc., 121, 65-73.
  • V. Howard and M. Reed, 1998. Unbiased Stereology. Three-dimensional measurement in microscopy. Microscopy handbooks 41, Bios Scientific Publishers, UK.
  • J. Russ, R. Dehoff, 1999. Practical Stereology, 2nd Edition, Plenum Press, New York.
  • L. Kubinova, X. W. Mao, J. Janacek, J. O. Archambeau, 2003. Stereology Techniques in Radiation Biology, Radiation Research 160, 110-119.


Stereology Toolkit for ImageScope Evaluation Version (29.6 Mbytes) >> Download <<

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