SYSTEMS AND METHODS FOR USING AN IMMUNOSTAINING MASK TO SELECTIVELY REFINE ISH ANALYSIS RESULTS

• US 20140153811A1
• Filed: 12/04/2012
• Published: 06/05/2014
• Est. Priority Date: 12/04/2012
• Status: Active Grant

• Current Assignee: Leica Microsystems CMS GmbH (Danaher Corporation)
• Original Assignee: General Electric Company
• Inventors: Seppo, Antti, Al-Kofahi, Yousef, Padfield, Dirk R.
• Granted Patent: US 9,135,694 B2
• Time in Patent Office: Days
• US Class Current: 382/133
• CPC Class Codes: G06T 2207/10064 Fluorescence image
• CPC Class Codes: G06T 2207/30024 Cell structures in vitro; Tissue sections in vitro
• CPC Class Codes: G06T 2207/30072 Microarray; Biochip, DNA array; Well plate
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/0014 using an image reference approach
• CPC Class Codes: G06V 20/695 Preprocessing, e.g. image segmentation
• CPC Class Codes: G06V 20/698 Matching; Classification

Abstract

A computer-implemented method of processing image data representing biological units in a tissue sample includes receiving a first image of the tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker, and receiving a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe. The method further includes classifying each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image, performing a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom, and filtering the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one class.

Claims

1. A computer-implemented method of processing image data representing biological units in a tissue sample, the computer including a processor, the method comprising:

receiving, by the processor, a first image of the tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker;

receiving, by the processor, a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe;

classifying, by the processor, each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image;

performing, by the processor, a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom; and

filtering, by the processor, the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one of the at least two classes only.

2. The method of claim 1, wherein the at least two classes include epithelial and non-epithelial.

3. The method of claim 1, further comprising registering, by the processor, locations of signals from the IF morphological marker in the first image with locations of signals from the fluorescent probe in the second image to produce a registered image.

4. The method of claim 3, further comprising segmenting, by the processor, the second image to identify nuclei in the tissue sample based on the locations of signals from the fluorescent probe in the second image.

5. The method of claim 4, wherein each biological unit corresponds to respective nuclei identified in the tissue sample.

6. The method of claim 4, wherein segmenting the nuclei includes applying, by the processor, a wavelet transform to the second image.

7. The method of claim 6, further comprising generating, by the processor, a Voronoi partition and rings generated from a threshold distance map from the nuclei in the second image in which each respective ring is constrained to at least partially surround only one nucleus in the tissue sample.

8. The method of claim 7, wherein generating the Voronoi partition in the second image enables generating local background regions from the second image using nuclei identified in the tissue sample as seeds of the Voronoi partition multiplied by a mask defined by each ring.

9. The method of claim 7, wherein the IF morphological marker is configured to target a cytokeratin (CK) protein, and wherein the method further comprises thresholding the first image using a threshold value in conjunction with Otsu'"'"'s thresholding method and estimating a minimum intensity level of an epithelial region in the first image.

10. The method of claim 9, further comprising changing the threshold value interactively by a user while observing a change in classification of each biological unit.

11. The method of claim 9, wherein each nucleus is classified as one of epithelial and non-epithelial by computing a mean cytokeratin (CK) intensity within the ring surrounding the nucleus and comparing the mean CK intensity to the estimated minimum intensity level of the epithelial region.

12. The method of claim 1, wherein the IF morphological marker includes a 4′

,6-diamidino-2-phenylindole (DAPI) stain.

13. The method of claim 1, wherein the IF morphological marker is configured to target at least one of a cytokeratin (CK) protein and a Human Epidermal Growth Factor Receptor 2 (HER2) protein.

14. The method of claim 1, wherein the fluorescent probe is configured to bind to least one of a HER2 gene and a Chromosome 17 centrometric repeat.

15. A non-transitory computer-readable medium having stored thereon computer-executable instructions that when executed by a computer cause the computer to:

receive a first image of a tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker;

receive a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe;

classify each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image;

perform a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom; and

filter the results of the FISH analysis to produce a subset of the results pertaining to biological units classified as epithelial only.

16. The computer readable medium of claim 15, further comprising computer-executable instructions that when executed by the computer cause the computer to:

register locations of signals from the IF morphological marker in the first image with locations of signals from the fluorescent probe in the second image to produce a registered image.

17. The computer readable medium of claim 16, further comprising computer-executable instructions that when executed by the computer cause the computer to:

segment the second image to identify nuclei in the tissue sample based on the locations of signals from the fluorescent probe in the second image.

18. A system for processing image data representing biological units in a tissue sample, the system comprising:

a processor;

an input in electrical communication with the processor and configured to receive the image data; and

a memory in electrical communication with the processor, the memory including computer-executable instructions that when executed by the processor cause the processor to;

receive a first image of a tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker;

receive a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe;

classify each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image;

perform a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom; and

filter the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one of the at least two classes only.

19. The system of claim 18, wherein the at least two classes include epithelial and non-epithelial.

20. The system of claim 18, wherein the memory further includes computer-executable instructions that when executed by the processor cause the processor to:

register locations of signals from the IF morphological marker in the first image with locations of signals from the fluorescent probe in the second image to produce a registered image.

21. The system of claim 20, wherein the memory further includes computer-executable instructions that when executed by the processor cause the processor to:

segment the second image to identify nuclei in the tissue sample based on the locations of signals from the fluorescent probe in the second image.

22. The system of claim 21, wherein each biological unit corresponds to respective nuclei identified in the tissue sample.

23. The system of claim 21, wherein the memory further includes computer-executable instructions that when executed by the processor cause the processor to apply a wavelet transform to the second image to segment the nuclei in the tissue sample.

24. The system of claim 23, wherein the memory further includes computer-executable instructions that when executed by the processor cause the processor to generate a Voronoi partition and rings generated from a threshold distance map from the nuclei in the second image in which each respective ring is constrained to at least partially surround only one nucleus in the tissue sample.

25. The system of claim 23, wherein the memory further includes computer-executable instructions that when executed by the processor cause the processor to generate local background regions from the second image using nuclei identified in the tissue sample as seeds of the Voronoi partition multiplied by a mask defined by each ring.

26. The system of claim 18, wherein the IF morphological marker includes a 4′

,6-diamidino-2-phenylindole (DAPI) stain.

27. The system of claim 18, wherein the IF morphological marker is configured to target at least one of a cytokeratin (CK) protein and a Human Epidermal Growth Factor Receptor 2 (HER2) protein.

28. The system of claim 18, wherein the fluorescent probe is configured to bind to least one of a HER2 gene and a Chromosome 17 centrometric repeat.