Automated assessment of glaucoma loss from optical coherence tomography

• US 10,354,384 B2
• Filed: 12/07/2016
• Issued: 07/16/2019
• Est. Priority Date: 05/04/2012
• Status: Active Grant

• Current Assignee: US Department of Veterans Affairs (Government of the United States of America)
• Original Assignee: University of Iowa Research Foundation
• Inventors: Abramoff, Michael, Sonka, Milan
• Primary Examiner(s): Dinh, Jack
• Application Number:
  US15/371,925
• Publication Number:
  US 20170236282A1
• Time in Patent Office: 951 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 3/1225 using coherent radiation
• CPC Class Codes: G06T 2207/10101 Optical tomography; Optical coherence tomography [OCT]
• CPC Class Codes: G06T 2207/30041 Eye; Retina; Ophthalmic
• CPC Class Codes: G06T 7/0016 involving temporal comparison

Abstract

Systems and methods for assessing glaucoma loss using optical coherence topography. One method according to an aspect comprises receiving optical coherence image data and assessing functional glaucoma damage from retinal optical coherence image data. In an aspect, the systems and methods can map regions and layers of the eye to determine structural characteristics to compare to functional characteristics.

Claims

1. A method comprising:

receiving a spectral-domain optical coherence tomography (SD-OCT) image;

determining a thickness of a macular ganglion cell layer (GCL) within at least one of one or more regions of interest of the SD-OCT image;

determining, for at least one of the one or more regions of interest, a set of correlations of the GCL thickness to each of a plurality of optic nerve head (ONH) regions;

selecting from the plurality of ONH regions, based on a highest correlation from among the set of correlations, an ONH region indicative of macular damage;

determining, for each of a plurality of nerve fiber bundle (NFB) regions within at least one of the one or more areas of interest, a connectivity path of the GCL to at least two nerve fiber bundle (NFB) segments within the NFB region; and

determining, for each NFB region, a path of a retinal ganglion cell-axonal complex (RGC-AC) segment with a highest cumulative correlation among all possible RGC-AC segment paths.

2. The method of claim 1, further comprising:

generating, based on one or more image grids and the SD-OCT image, one or more regions of interest of the SD-OCT image.

3. The method of claim 2, wherein the one or more retinal grids comprise a NFB grid, a macular grid, or an ONH grid.

4. The method of claim 3, wherein the macular grid comprises a subset of one or more regions of the NFB grid.

5. The method of claim 3, wherein each region of the one or more regions of interest comprises a size based on a scaling factor and the NFB grid, and wherein the macular grid comprises a subset of one or more regions of the NFB grid.

6. The method of claim 5, wherein the scaling factor comprises a distance between a first portion of the SD-OCT image and a second portion of the SD-OCT image.

7. The method of claim 6, wherein the first portion of the SD-OCT image is indicative of a fovea, and wherein the second portion of the SD-OCT image is indicative of a center of a neural canal opening.

8. The method of claim 7, wherein the NFB grid comprises a width and a height.

9. The method of claim 8, wherein the width of the NFB grid and the height of the NFB grid are each based on a ratio comprising the distance between the first portion of the SD-OCT image and the second portion of the SD-OCT.

10. The method of claim 1, further comprising:

mapping a connectivity of the GCL to a neural rim of an ONH within at least one of the one or more regions of interest;

mapping a connectivity of the GCL to an initial NFB segment and a final NFB segment to an ONH within at least one of the one or more regions of interest; and

mapping a connectivity of at least two NFBs within a NFB region of at least one of the one or more areas of interest.

11. A system, comprising:

a memory having computer-executable instructions encoded thereon; and

at least one processor functionally coupled to the memory and configured, by the computer-executable instructions, to;

receive a spectral-domain optical coherence tomography (SD-OCT) image;

determine a thickness of a macular ganglion cell layer (GCL) within at least one of one or more regions of interest of the SD-OCT image;

determine, for at least one of the one or more regions of interest, a set of correlations of the GCL thickness to each of a plurality of optic nerve head (ONH) regions;

select from the plurality of ONH regions, based on a highest correlation from among the set of correlations, an ONH region indicative of macular damage;

determine, for each of a plurality of nerve fiber bundle (NFB) regions within at least one of the one or more areas of interest, a connectivity path of the GCL to at least two nerve fiber bundle (NFB) segments within the NFB region; and

determine, for each NFB region, a path of a retinal ganglion cell-axonal complex (RGC-AC) segment with a highest cumulative correlation among all possible RGC-AC segment paths.

12. The system of claim 11, wherein the processor is further configured, by the computer executable instructions, to:

generate, based on one or more image grids and the SD-OCT image, one or more regions of interest of the SD-OCT image.

13. The system of claim 12, wherein the one or more retinal grids comprise a NFB grid, a macular grid, or an ONH grid.

14. The system of claim 13, wherein the macular grid comprises a subset of one or more regions of the NFB grid.

15. The system of claim 13, wherein each region of the one or more regions of interest comprises a size based on a scaling factor and the NFB grid, and wherein the macular grid comprises a subset of one or more regions of the NFB grid.

16. The system of claim 15, wherein the scaling factor comprises a distance between a first portion of the SD-OCT image and a second portion of the SD-OCT image.

17. The system of claim 16, wherein the first portion of the SD-OCT image is indicative of a fovea, and wherein the second portion of the SD-OCT image is indicative of a center of a neural canal opening.

18. The method of claim 13, wherein the NFB grid comprises a width and a height.

19. The method of claim 18, wherein the width of the NFB grid and the height of the NFB grid are each based on a ratio comprising the distance between the first portion of the SD-OCT image and the second portion of the SD-OCT.

20. The system of claim 11, wherein the processor is further configured, by the computer executable instructions, to:

map a connectivity of the GCL to a neural rim of an ONH within at least one of the one or more regions of interest;

map a connectivity of the GCL to an initial NFB segment and a final NFB segment to an ONH within at least one of the one or more regions of interest; and

map a connectivity of at least two NFBs within a NFB region of at least one of the one or more areas of interest.