System and method for tissue differentiation in imaging

• US 10,359,271 B2
• Filed: 05/05/2017
• Issued: 07/23/2019
• Est. Priority Date: 12/05/2012
• Status: Active Grant

• Current Assignee: Perimeter Medical Imaging, Inc. (Perimeter Medical Imaging AI, Inc.)
• Original Assignee: Perimeter Medical Imaging, Inc. (Perimeter Medical Imaging AI, Inc.)
• Inventors: Rempel, David
• Primary Examiner(s): LaPage, Michael P
• Application Number:
  US15/588,243
• Publication Number:
  US 20170299370A1
• Time in Patent Office: 809 Days
• CPC Class Codes: A61B 3/0025 characterised by electronic signal processing, e.g. eye models
• CPC Class Codes: A61B 3/102 for optical coherence tomography [OCT]
• CPC Class Codes: A61B 5/0066 Optical coherence imaging
• CPC Class Codes: G01B 9/02041 characterised by particular imaging or detection techniques
• CPC Class Codes: G01B 9/02091 Tomographic interferometers, e.g. based on optical coherence
• CPC Class Codes: G01N 21/01 Arrangements or apparatus for facilitating the optical investigation
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 21/4795 spatially resolved investigating of object in scattering medium in vivo A61B

Abstract

Various embodiments of systems and methods are described herein for obtaining wide field OCT images and other types of image data from at least one portion of a sample. Various embodiments of systems, and methods are described herein for assessing a degree of differentiation for a second region of an OCT image of a tissue sample with respect to another first. Various embodiments of a sample container are also described herein for containing a tissue sample and maintaining the tissue sample in a defined orientation during imaging or handling.

Claims

1. A method of assessing a degree of differentiation for a second region of an OCT image of a tissue sample with respect to a first region, wherein the method comprises:

receiving OCT image data for the OCT image, the OCT image being a 3D OCT image comprising a plurality of B-scan images;

identifying a boundary in the OCT image to identify the second region in the OCT image, the second region being disposed below the boundary;

identifying a first set of OCT image data corresponding to the first region and a second set of OCT image data corresponding to the second region;

generating a first optical dataset based on measurements of at least one first optical characteristic for the first set of OCT image data;

generating a second optical dataset based on measurements of the at least one first optical characteristic for the second set of OCT image data;

determining a margin width in B-scan data for the OCT image, the margin width corresponding to a shortest distance between the boundary and a surface of the tissue sample;

determining when the margin width is less than a margin threshold;

receiving a known optical dataset generated based on measurements of the at least one first optical characteristic for a known set of OCT image data corresponding to a known region in a stored OCT image; and

determining a discrimination score based on the second optical dataset and the known optical dataset to indicate a degree of difference between the second region and the known region and comparing the discrimination score to a predefined threshold to identify a high degree of difference between the second region and the known region which indicates that the second and known region comprise different tissue types.

2. The method of claim 1, wherein the method comprises identifying the first region above the boundary in the OCT image.

3. The method of claim 1, wherein the method comprises identifying a reference region in a reference OCT image as the first region, the reference OCT image corresponding to an image of a known tissue type.

4. The method of claim 1, wherein the method further comprises calculating the discrimination score using one of a machine learning method or an optimization method.

5. The method of claim 1, wherein identifying the boundary comprises:

measuring at least one second optical characteristic for a plurality of points in the OCT image; and

comparing the at least one measured second optical characteristic for different spatial points in the OCT image to a region threshold value in order to determine potential boundaries for the first region or the second region depending on the result of the comparison.

6. The method of claim 5, wherein the method comprises receiving an input from a user to identify the boundary after providing the potential boundaries to the user.

7. The method of claim 5, wherein the method comprises identifying the boundary automatically by generating the two regions in the OCT image by applying edge detection to the potential boundaries of the points in the OCT image;

and identifying the boundary to separate the first and second regions.

8. The method of claim 5, the method comprises:

identifying a plurality of boundaries across the B-scan images;

determining a consistent boundary based the boundaries of the plurality of boundaries that are consistent with one another, the consistent boundaries being boundaries of adjacent B-scan images that are within a certain range of pixels of one another; and

identifying the boundaries in the plurality of B-scan images that are consistent boundaries.

9. The method of claim 1, wherein the method further comprises repeating the identifying, generating and comparing acts for all B-scan images to obtain a plurality of discrimination scores corresponding to the plurality of B-scan images.

10. The method of claim 1, wherein the OCT image data comprises OCT signal intensity data and any measured optical characteristics are at least one of an attenuation of the OCT signal intensity data, a fluctuation of the OCT signal intensity data, an area under the profile of an A-scan in the OCT signal intensity data, a number of signal maxima in the OCT signal intensity data, a mean distance between signal maxima in the OCT signal intensity data, a standard deviation of the peak distance in the OCT signal intensity data, a mean frequency of the OCT signal intensity data, and a standard deviation of the frequencies of the OCT signal intensity data.

11. The method of claim 1, further comprising:

identifying a subsequent boundary in the OCT image distinguishing between the second region and a third region;

identifying a third set of OCT image data corresponding to the third region;

generating a third optical dataset based on the third set of OCT image data; and

comparing the second optical dataset and the third optical dataset to identify a degree of difference between the second region and the third region.

12. The method of claim 1, wherein the method further comprises displaying the degree of difference between the first region and the second region on the OCT image.

13. The method of claim 1, wherein the method further comprises displaying a color mask on the OCT image representing the degree of difference between the first region and the second region.

14. The method of claim 13, wherein the method further comprises displaying the margin width on the OCT image.

15. A system for assessing a degree of differentiation for a second region of an OCT image of a tissue sample with respect to a first region, the system comprising:

a data interface configured to receive OCT image data for the OCT image, the OCT image being a 3D OCT image comprising a plurality of B-scan images;

a user interface configured to receive at least one input from a user and to display at least one output to the user; and

a processing module coupled to the data interface and the user interface, the processing module being configured;

to identify a boundary in the OCT image to identify the second region in the OCT image, the second region being disposed below the boundary;

to identify a first set of OCT image data corresponding to the first region and a second set of OCT image data corresponding to the second region;

to generate a first optical dataset based on measurements of at least one first optical characteristic for the first set of OCT image data;

to generate a second optical dataset based on measurements of the at least one first optical characteristic for the second set of OCT image data;

to determine a margin width in B-scan data for the OCT image, the margin width corresponding to a shortest distance between the boundary and a surface of the tissue sample;

to determine when the margin width is less than a margin threshold;

to receive a known optical dataset generated based on measurements of the at least one first optical characteristic for a known set of OCT image data corresponding to a known region in a stored OCT image; and

to determine a discrimination score based on the second optical dataset and the known optical dataset and compare the discrimination score to a predefined threshold to identify a high degree of difference between the second region and the known region which indicates that the second and known region comprise different tissue types.

16. The system of claim 15, wherein the processing module is further configured to identify the boundary by:

measuring at least one second optical characteristic for a plurality of points in the OCT image; and

comparing the at least one measured second optical characteristic for different spatial points in the OCT image to a region threshold value in order to determine potential boundaries for the first region or the second region depending on the result of the comparison.

17. The system of claim 16, wherein the processing module is further configured to identify the boundary automatically by generating the two regions in the OCT image by applying edge detection to the potential boundaries of the points in the OCT image;

and identifying the boundary to separate the first and second regions.

18. The system of claim 15, wherein the processing module is further configured to:

identify a plurality of boundaries across the B-scan images;

determine a consistent boundary based on the boundaries of the plurality of boundaries that are consistent with one another, the consistent boundaries being boundaries of adjacent B-scan images that are within a certain range of pixels of one another; and

identify the boundaries in the plurality of B-scan images that are consistent boundaries.

19. The system of claim 15, wherein the processing module is configured to calculate the discrimination score using one of a machine learning method or an optimization method.

20. A non-transitory computer-readable medium storing computer-executable instructions for causing a processor to perform a method of assessing a degree of differentiation for a second region of an OCT image of a tissue sample with respect to a first region, the method comprising:

receiving OCT image data for the OCT image, the OCT image being a 3D OCT image comprising a plurality of B-scan images;

identifying a boundary in the OCT image to identify the second region in the OCT image, the second region being disposed below the boundary;

identifying a first set of OCT image data corresponding to the first region and a second set of OCT image data corresponding to the second region;

generating a first optical dataset based on measurements of at least one first optical characteristic for the first set of OCT image data;

generating a second optical dataset based on measurements of the at least one first optical characteristic for the second set of OCT image data;

determining a margin width in B-scan data for the OCT image, the margin width corresponding to a shortest distance between the boundary and a surface of the tissue sample;

determining when the margin width is less than a margin threshold;

receiving a known optical dataset generated based on measurements of the at least one first optical characteristic for a known set of OCT image data corresponding to a known region in a stored OCT image; and

determining a discrimination score based on the second optical dataset and the known optical dataset to indicate a degree of difference between the second region and the known region and comparing the discrimination score to a predefined threshold to identify a high degree of difference between the second region and the known region which indicates that the second and known region comprise different tissue types.