Pathological tissue mapping

• US 20050165290A1
• Filed: 11/17/2004
• Published: 07/28/2005
• Est. Priority Date: 11/17/2003
• Status: Active Grant

• Current Assignee: David Sans
• Original Assignee: Aureon Biosciences, Inc.
• Inventors: Saidi, Olivier, Teverovskiy, Mikhail, Kotsianti, Angeliki
• Granted Patent: US 7,483,554 B2
• Time in Patent Office: Days
• US Class Current: 600/407
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/20036 Morphological image processing
• CPC Class Codes: G06T 2207/30024 Cell structures in vitro; Tissue sections in vitro
• CPC Class Codes: G06T 2207/30056 Liver; Hepatic
• CPC Class Codes: G06T 2207/30061 Lung
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/155 involving morphological operators
• CPC Class Codes: G06T 7/187 involving region growing; involving region merging; involving connected component labelling
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume
• CPC Class Codes: G06V 20/69 Microscopic objects, e.g. biological cells or cellular parts

Abstract

Embodiments of the present invention are directed to quantitative analysis of tissues enabling the measurement of objects and parameters of objects found in images of tissues including perimeter, area, and other metrics of such objects. Measurement results may be input into a relational database where they can be statistically analyzed and compared across studies. The measurement results may be used to create a pathological tissue map of a tissue image, to allow a pathologist to determine a pathological condition of the imaged tissue more quickly.

Claims

1. A method for tissue analysis comprising:

acquisition of a tissue image corresponding to a tissue sample;

segmentation of the image into a plurality of objects;

classifying the plurality of objects into one or more object types; and

quantifying at least one parameter corresponding to at least one first object type to produce a quantification result for each corresponding object of the first object type.

2. The method according to claim 1, wherein the parameter is selected from the group consisting of:

a size, a shape, a color, spacing, color intensity, luminescence, an area a relationship to a second object type and combinations of the foregoing.

3. The method according to claim 1, wherein an object type is selected from the group consisting of:

a basic object, a composite object, a cell, and a cell component.

4. The method according to claim 1, further comprising:

establishing a range of the quantification results;

dividing the range into a plurality of bins, wherein each bin represents a visual indicator for producing a modified image of the tissue image;

assigning each object of the first object type to a bin based on the quantification result for each object; and

modifying corresponding pixels for each object of the first object type in the modified image with the visual indicator of the bin corresponding to the respective object.

5. The method according to claim 1, wherein the at least one parameter is selected from the group consisting of:

a second classified object, a color, a shape and a predetermined area.

6. The method according to claim 4, further comprising classifying the tissue sample based on the modified image.

7. The method according to claim 6, wherein the tissue is classified as normal or abnormal.

8. The method according to claim 4, wherein the visual indicator is selected from the group consisting of:

color, color intensity, size, shape, symbol, letter and number.

9. The method according to claim 4, wherein the tissue image comprises an image of liver tissue and the classified first object type comprises a hepatocyte and the parameter comprises fat content.

10. The method according to claim 4, wherein the method is used to identify toxic effect or response.

11. The method according to claim 4, wherein the method is be used to identify immunological reactions.

12. The method according to claim 4, wherein the method may be used to identify morphological lesions caused by disease selected from the group consisting of:

acute, sub-acute or chronic hepatitis;

inflammatory or necrotic cholestasis;

fibrosis;

granulomatous hepatitis;

macro or microvesicular steatosis;

vascular lesions; and

hepatic tumors.

13. The method according to claim, 6, further comprising training a neural network and/or a support vector machine using the modified image.

14. The method according to claim 12, wherein quantification comprises a ratio of the area of fat contained in each hepatocyte, to the area of the respective hepatocyte.

15. A method for tissue analysis comprising:

acquisition of a tissue image corresponding to a tissue sample;

segmentation of the image into a plurality of objects;

classifying the plurality of objects into one or more object types;

quantifying at least one parameter corresponding to at least one first object type to produce a quantification result for each corresponding object of the first object type;

establishing a range of the quantification results;

dividing the range into a plurality of bins, wherein each bin represents a visual indicator for producing a modified image of the tissue image;

assigning each object of the first object type to a bin based on the quantification result for each object; and

modifying corresponding pixels for each object of the first object type in the modified image with the visual indicator of the bin corresponding to the respective object.

16. A computer application program operable on a computer system for enabling the computer system to perform a method for tissue analysis, the method comprising:

acquisition of a tissue image corresponding to a tissue sample;

segmentation of the image into a plurality of objects;

classifying the plurality of objects into one or more object types;

quantifying at least one parameter corresponding to at least one first object type to produce a quantification result for each corresponding object of the first object type;

establishing a range of the quantification results;

dividing the range into a plurality of bins, wherein each bin represents a visual indicator for producing a modified image of the tissue image;

assigning each object of the first object type to a bin based on the quantification result for each object; and

modifying corresponding pixels for each object of the first object type in the modified image with the visual indicator of the bin corresponding to the respective object.

17. The computer application program according to claim 16, where the method further includes displaying and/or printing the modified image.

18. A computer readable medium having one or more computer application programs and/or computer instructions for enabling a computer system to perform a method for tissue analysis, the method comprising:

acquisition of a tissue image corresponding to a tissue sample;

segmentation of the image into a plurality of objects;

classifying the plurality of objects into one or more object types;

quantifying at least one parameter corresponding to at least one first object type to produce a quantification result for each corresponding object of the first object type;

establishing a range of the quantification results;

dividing the range into a plurality of bins, wherein each bin represents a visual indicator for producing a modified image of the tissue image;

assigning each object of the first object type to a bin based on the quantification result for each object; and

modifying corresponding pixels for each object of the first object type in the modified image with the visual indicator of the bin corresponding to the respective object.

19. The computer readable media according to claim 18, where the method further includes displaying and/or printing the modified image.

20. A computer system for performing a method for tissue analysis, the method comprising:

acquisition means for acquiring a tissue image corresponding to a tissue sample;

segmentation means for segmenting the image into a plurality of objects;

classifying means for classifying the plurality of objects into one or more object types;

quantifying means for quantifying at least one parameter corresponding to at least one first object type to produce a quantification result for each corresponding object of the first object type;

establishment means for establishing a range of the quantification results;

dividing means for dividing the range into a plurality of bins, wherein each bin represents a visual indicator for producing a modified image of the tissue image;

assigning means for assigning each object of the first object type to a bin based on the quantification result for each object; and

modifying means for modifying corresponding pixels for each object of the first object type in the modified image with the visual indicator of the bin corresponding to the respective object.

21. A system for tissue analysis comprising:

an input for inputting a digital image;

an output comprising at least one of a display and a printer;

a processor for processing computer instructions and data, the processor operating to;

segmenting the image into a plurality of objects;

classifying the plurality of objects into one or more object types;

quantifying at least one parameter corresponding to at least one first object type to produce a quantification result for each corresponding object of the first object type;

establishing a range of the quantification results;

dividing the range into a plurality of bins, wherein each bin represents a visual indicator for producing a modified image of the tissue image;

assigning each object of the first object type to a bin based on the quantification result for each object; and

modifying corresponding pixels for each object of the first object type in the modified image with the visual indicator of the bin corresponding to the respective object.