Method and a system for determining a target in a biological sample by image analysis

• US 8,731,845 B2
• Filed: 08/14/2009
• Issued: 05/20/2014
• Est. Priority Date: 08/15/2008
• Status: Active Grant

• Current Assignee: Visiopharm A/S
• Original Assignee: Visionpharm A/S
• Inventors: Grunkin, Michael, Foged, Niels Tkker
• Primary Examiner(s): Lin, Jerry
• Application Number:
  US13/058,385
• Publication Number:
  US 20110234812A1
• Time in Patent Office: 1,740 Days
• Field of Search: 702/21
• US Class Current: 702/21
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06V 20/695 Preprocessing, e.g. image segmentation
• CPC Class Codes: G16B 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks

Abstract

The present invention relates to methods for identifying and optionally quantifying stained cells in a cell or tissue sample and determining expression level of a target, as well as methods for determining staining intensity and/or staining quality of a stained sample, calibrating digital imaging apparatus using the methods, as well as systems and a computer readable mediums therefore. In particular the invention relates to a method for identifying stained targets in a sample and uses of said method, said method comprising providing at least one digital representation of the sample wherein the sample has been stained with a stain staining a target, and filtering the digital representation with at least three filters, each of said filters comprising filter constants), applying said filter constant(s) to each pixels in at least a subset of the digital representation and determining an eigensolution based on the filter output for each pixel in the subset, wherein said filters are capable of enhancing the stained targets obtaining a filtered representation, segmenting the digital representation based on information from the optionally normalized filtered representation into stained cell targets and background, optionally combining with the digital representation, thereby obtaining a segmented representation, wherein said segmented representation discriminates between stained targets and other structures in the digital representation, and from said segmented representation identifying the stained targets in the sample.

Claims

1. An automated system for identifying one or more stained targets in a biological sample comprising a slide scanner, and a view screen, and further, comprising, in combination:

a database capable of including a plurality of digital representations of a plurality of biological samples, wherein said samples have been stained with a stain so as to define a stained target; and

a software module for analyzing a plurality of pixels from at least one of said digital representations of said biological samples, and carrying out the following method for identifying one or more stained targets in said samples, said method comprising;

a) providing at least one digital representation of the sample wherein the sample has been stained with a stain so as to define said stained target, and

b) filtering the digital representation with at least three filters, each of said filters comprising filter constant(s), applying said filter constant(s) to each pixels in at least a subset of the digital representation and determining an eigensolution based on the filter output for each pixel in the subset, wherein said filters are capable of enhancing the stained targets obtaining a filtered representation,

c) segmenting the digital representation based on information from the filtered representation into stained cell targets and background, thereby obtaining a segmented representation, wherein said segmented representation discriminates between stained targets and other structures in the digital representation, and;

d) from said segmented representation, identifying the stained targets in the sample.

2. The system according to claim 1, wherein the filter constant(s) are estimated polynomial coefficients.

3. The system according to claim 2, wherein the polynomial coefficients are estimated from a second order polynomial, a fourth order polynomial, or a sixth order polynomial.

4. The system according to claim 1, wherein at least three filter constants are applied to the pixels in the subset.

5. The system according to claim 1, wherein the subset correspond to the entire digital representation.

6. The system according to claim 1, wherein the filter constant(s) is(are) determined prior to analysing a plurality of representations and the same constant(s) are used for each digital representation.

7. The system according to claim 1, wherein the filter constant(s) is(are) determined for each digital representation.

8. The system according to claim 2, wherein a Hessian matrix is determined for the polynomial coefficient, and the Eigensolution is determined from said Hessian matrix.

9. The system according to claim 2, wherein a least-squares estimator is used to determine the coefficient(s) of the polynomial.

10. The system according to claim 9, wherein the least-squares estimator is a weighted least-squares estimator.

11. The system according to claim 1, wherein the sample is a tissue sample.

12. The system according to claim 1, wherein the sample is a sample suspected of comprising cancer cells.

13. The system according claim 11, wherein the cancer cells are breast cancer cells.

14. The system of claim 1, wherein the stain is a chromophore or a fluorophore.

15. The system according to claim 1, wherein the stain stains for a target associated with the cell membrane.

16. The system according to claim 15, wherein the target is a protein associated with the cell membrane.

17. The system of claim 16, wherein the protein is selected from the HER-family, cytokeratins;

and CD-antigens.

18. The system according to claim 1, wherein the stain stains for a target associated with linear structures outside the cell.

19. The system according to claim 18, wherein the target is a protein lining vessels or bronchi.

20. The system according to claim 1, wherein the Eigensolution is the first Eigen value.

21. The system according to claim 20, wherein the stain stains for at least one blob-shaped target in, on or outside the cell(s).

22. The system according to claim 1, wherein the Eigensolution is the second Eigen value.

23. The system according to claim 1, wherein the filtered representation is normalized with the intensity representation.

24. The system according to claim 1, wherein the segmented representation is post-processed.

25. The system according to claim 24, wherein the post-processing relates to elimination of stained cell membranes, extracellular linear structures or blobs having an area below or higher than a predefined area.

26. The system according to claim 1, wherein a measure for connectivity is computed as area of cell membranes divided with number of connected cell membranes.

27. The system according to claim 1, wherein the staining intensity in the segmented representation is calculated.

28. The system according to claim 27, wherein the staining intensity is calculated as the average staining intensity.

29. The system according to claim 28, wherein the staining intensity is calculated as the average staining intensity with respect to a red chromaticity value or a normalized brown value.

30. The system according to claim 2, wherein a window size for the polynomial is fitted to be larger than the size of the subset.

31. The system according to claim 1 suitable for classifying a sample, said system comprising in said control module instructions for carrying out a method of identifying one or more stained targets in said sample by a method as defined in claim 1, and based on the identified stained targets classifying the sample in relation to two or more groups of samples.

32. The system according to claim 31, wherein the classification is performed by statistically calculating the likelihood that the sample belongs to a specific group.

33. The system according to claim 31, wherein a quantification of stained targets are performed, and the classification is based on the quantification.

34. The system according to claim 1, wherein the slide scanner is a virtual slide scanner.

35. The system according to claim 1, wherein the filtered representation is normalized before the step of segmentation.

36. The system according to claim 1, wherein the filtered representation is combined with the digital representation.

37. The system according to claim 1, further comprising a slide loader, a barcode reader, a microscope and a stage.