Method and system for three-dimensional thickness mapping of corneal micro-layers and corneal diagnoses

• US 10,368,735 B2
• Filed: 01/11/2018
• Issued: 08/06/2019
• Est. Priority Date: 01/11/2017
• Status: Active Grant

• Current Assignee: University of Miami
• Original Assignee: University of Miami
• Inventors: Abou Shousha, Mohamed, Elsawy, Amr Saad Mohamed
• Primary Examiner(s): Yang, Weiwen
• Application Number:
  US15/868,856
• Publication Number:
  US 20180192866A1
• Time in Patent Office: 572 Days
• Field of Search: None
• CPC Class Codes: A61B 3/0025 characterised by electronic signal processing, e.g. eye models
• CPC Class Codes: A61B 3/1005 for measuring distances inside the eye, e.g. thickness of the cornea A61B3/11 takes precedence
• CPC Class Codes: A61B 3/14 Arrangements specially adapted for eye photography apparatus or arrangements for taking photographs per se G03B
• CPC Class Codes: G06T 15/04 Texture mapping
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/10101 Optical tomography; Optical coherence tomography [OCT]
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20216 Image averaging
• CPC Class Codes: G06T 2207/30041 Eye; Retina; Ophthalmic
• CPC Class Codes: G06T 3/14 Transformations for image registration, e.g. adjusting or mapping for alignment of images
• CPC Class Codes: G06T 3/4038 Image mosaicing, e.g. composing plane images from plane sub-images
• CPC Class Codes: G06T 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 7/0014 using an image reference approach
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/32 using correlation-based methods
• CPC Class Codes: G06T 7/60 Analysis of geometric attributes
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume

Abstract

Techniques for improved diagnosis, treatment, and monitoring of corneal pathologies use enhanced mapping of the cornea or corneal regions, to develop three-dimensional mapping of corneal thickness, while retaining particular corneal micro-layer thickness data. Anterior and posterior surface identifications, along with surface apex determinations, are used for registration of segmentation of these micro-layers. 3D heat maps and bull'"'"'s-eye maps are generated from resulting thickness date. The maps provided enhanced evaluation and diagnosis of a corneal pathologies, such as keratoconus, pellucid marginal degeneration, post-refractive surgery ectasia, keratoglobus, corneal transplant rejection and corneal transplant failed grafts, Fuchs dystrophy, corneal limbal stem cell deficiency, dry eye syndrome, and post-corneal collagen crosslinking evaluation.

Claims

1. A computer-implemented method of evaluating an eye, the method comprising:

segmenting, using an image processor, a plurality of high-resolution images of a cornea of the eye, to identify one or more of the plurality of biologically-defined micro-layers of the cornea, the plurality of high-resolution images comprising a plurality of images for a plurality of biologically-defined micro-layers of the cornea;

determining thickness data for each of the identified one or more of the plurality of biologically-defined micro-layers, from the segmentation of the plurality of high-resolution images;

developing, from the thickness data for each of the identified one or more of the plurality of biologically-defined micro-layers, a thickness map, the thickness map identifying differences in corneal thickness across the identified biologically-defined micro-layer; and

displaying the thickness map, wherein the thickness map comprises one of;

(i) a bull'"'"'s-eye map of micro-layer thickness mean, variance, and standard deviation;

(ii) a bull'"'"'s-eye map or heat map of a ratio or comparison of micro-layer thickness to a total corneal thickness;

(iii) a bull'"'"'s-eye map of a ratio or comparison of thickness of the micro-layer to normative data;

(iv) a bull'"'"'s-eye map of a ratio or comparison between micro-layer thickness of a region to the thickness of the micro-layer in another region;

(v) a heat map of a ratio between micro-layer thickness of a region to the thickness of the micro-layer in another region;

(vi) a heat map or bulls-eye map of basal epithelial layer, the heat map having a color coding that differentiates minimum thickness regions of the basal epithelial layer, normal thickness regions of the basal epithelial layer, and maximum thickness regions of the basal epithelial layer;

or

(vii) a heat map or bull'"'"'s-eye map of a collagen crosslinking layer.

2. The method of claim 1, wherein the identified one or more of the plurality biologically-defined micro-layers comprises

an epithelium,

a basal epithelial layer,

a Bowman'"'"'s layer,

one or more endothelial/Descemet'"'"'s layers complex, and/or

a collagen crosslinking layer.

3. The method of claim 1, wherein the plurality of high-resolution images comprises

(i) a plurality of images of the cornea each captured with the eye looking in a different direction, where a wide scan of the cornea is formed by stitching images together, or

(ii) a plurality of wide scan images of the cornea corrected for optical distortion.

4. The method of claim 1, wherein the plurality of high-resolution images comprises images of the cornea captured from a wide angle lens capture of the optical imaging system.

5. The method of claim 1, wherein the thickness map is correlated to a diagnosable condition of the cornea, wherein the diagnosable condition is selected from the group consisting of keratoconus, pellucid marginal degeneration, post- refractive surgery ectasia, corneal transplant rejection and corneal transplant failed grafts, Fuchs dystrophy, limbal stem cell deficiency, dry eye syndrome, and post-operative collagen crosslinking procedure.

6. The method of claim 1, wherein the thickness map is correlated to a diagnosable condition of the cornea,wherein the diagnosable condition comprises keratoconus, pellucid marginal degeneration, and/or post-refractive surgery ectasia, and wherein the identified one or more of the plurality biologically-defined micro-layers comprises the Bowman'"'"'s layer, wherein the thickness map comprises:

(i) bull'"'"'s-eye map of a ratio or comparison of thickness of the micro-layer to normative data, wherein the bull'"'"'s-eye map is of a ratio or comparison between measured thickness of the Bowman'"'"'s layer corresponding to a bull'"'"'s-eye section and normal thickness of the Bowman'"'"'s layer,

(ii) the heat map of a ratio between micro-layer thickness of a region to the thickness of the micro-layer in another region, wherein the heat map is of a ratio between measured thickness and normal thickness of the Bowman'"'"'s layer, or

(iii) the bull'"'"'s-eye map of a ratio or comparison between micro-layer thickness of a region to the thickness of the micro-layer in another region, wherein the bull'"'"'s-eye map is of a ratio or comparison between measured thickness in a region of the Bowman'"'"'s layer corresponding to a bull'"'"'s-eye section and that measured in another region of the Bowman'"'"'s layer.

7. The method of claim 1, wherein the thickness map is correlated to a diagnosable condition of the cornea, wherein the diagnosable condition comprises Fuchs dystrophy and/or corneal graft health, rejection and failure, and wherein the identified one or more of the plurality biologically-defined micro-layers comprises Endothelium/Descemet'"'"'s layer of the cornea, wherein the thickness map comprises:

(i) the bull'"'"'s-eye map of a ratio or comparison of thickness of the micro-layer to normative data, wherein the bull'"'"'s-eye map is of a ratio or comparison between measured thickness of the Endothelium/Descemet'"'"'s layer corresponding to a bull'"'"'s-eye section and normal thickness of the Endothelium/Descemet'"'"'s layer,

(ii) the heat map of a ratio between micro-layer thickness of a region to the thickness of the micro-layer in another region, wherein the heat map is of a ratio between measured thickness and normal thickness of the Endothelium/Descemet'"'"'s layer, or

(iii) the bull'"'"'s-eye map of a ratio or comparison between micro-layer thickness of a region to the thickness of the micro-layer in another region, wherein the bull'"'"'s-eye map is of a ratio or comparison between measured thickness in a region of the Endothelium/Descemet'"'"'s layer corresponding to a bull'"'"'s-eye section and that measured in another region of the Endothelium/Descemet'"'"'s layer.

8. The method of claim 1, wherein the thickness map is correlated to a diagnosable condition of the cornea, wherein the diagnosable condition comprises dry eye, and wherein the identified one or more of the plurality biologically-defined micro-layers comprises an epithelium, a basal epithelial layer, a Bowman'"'"'s layer, and one or more endothelial/Descemet'"'"'s layers complex of the cornea, wherein the thickness map comprises the bull'"'"'s-eye map of a ratio or comparison between micro-layer thickness of a region to the thickness of the micro-layer in another region, wherein the bull'"'"'s-eye map is of the corneal epithelium including an irregularity indication of changes in thickness across the corneal epithelium corresponding to a bull'"'"'s-eye section that indicates differences in concentration of thickness irregularities across different regions of the cornea.

9. The method of claim 8, wherein the different regions of the corneal epithelium comprise

(1) a central portion of the cornea corresponding to the diagnosable condition being aqueous deficiency,

(2) a lower or upper portion of the cornea corresponding to the diagnosable condition being lipid deficiency dry eye syndrome or Meibomian gland dysfunction.

10. The method of claim 1, wherein the thickness map is correlated to a diagnosable condition of the cornea, wherein the diagnosable condition comprises limbal stem cell deficiency, basal epithelial cells thinning, or absence indicates corneal limbal stem cell deficiency, and wherein the identified one or more of the plurality biologically-defined micro-layers of the cornea comprises basal epithelial layer, wherein the thickness map comprises the bull'"'"'s-eye map or heat map of the basal epithelial layer having a color coding that differentiates minimum thickness regions of the basal epithelial layer, normal thickness regions of the basal epithelial layer, and maximum thickness regions of the basal epithelial layer.

11. The method of claim 1, wherein segmenting the plurality of high-resolution images, to identify image data for each of the plurality of biologically-defined micro-layers of the cornea further comprises:

(a) performing an image registration on the plurality of high-resolution images by

(i) identifying an anterior surface of one of the plurality of biologically-defined micro-layers of the cornea, the anterior surface being a registration reference surface,

(ii) comparing a plurality of image frames in the plurality of high-resolution images to the anterior surface,

(iii) extracting frames that do not satisfy a registration condition, and

(iv) for the remaining frames performing a summation and averaging process to produce a high-resolution composite image of the one of the plurality of biologically-defined micro-layers.

12. The method of claim 11, further comprising:

(b) identifying one or more contrast transition surfaces for the high-resolution composite image, the transition surfaces corresponding to interfaces between the different corneal micro-layers;

(c) performing (a) for an adjacent biologically-defined micro-layer to identify a second high-resolution composite image, the adjacent biologically-defined micro-layer being identified by the one or more contrast transition surfaces; and

(d) performing (c) for each biologically-defined micro-layer of the cornea.

13. The method of claim 12, wherein (b) is performed to identify a transition to an anterior interface of the epithelium, an epithelium/basal epithelial layer interface, a basal epithelium/Bowman'"'"'s interface, Bowman'"'"'s/stroma interface, an anterior interface of the endothelial/Descemet'"'"'s layers, an interface of the endothelial/Descemet'"'"'s layers, an aqueous humor, and/or a collagen crosslinking layer interface.

14. The method of claim 1, wherein segmenting the plurality of high-resolution images further comprises:

(a) performing an image registration on the plurality of high-resolution images by, for each of the plurality of high-resolution images,

(i) identifying an anterior surface of the cornea and a posterior surface of the cornea, and

(ii) matching the anterior surface of the cornea and the posterior surface of the cornea to an anterior surface and a posterior surface, respectively, of a reference frame; and

(b) performing a summation and averaging process to produce a high-resolution composite image from the registered plurality of high-resolution images, where the high-resolution composite image is of the cornea.

15. The method of claim 14, wherein performing the image registration on the plurality of high-resolution images further comprises, for each of the plurality of high-resolution images, identifying an apex of the cornea and matching the apex of the cornea to an apex of a reference frame.

16. The method of claim 14, wherein segmenting the plurality of high-resolution images further comprises:

identifying an anterior surface and a posterior surface of the high-resolution composite image;

flattening the high-resolution composite image using the anterior surface;

flattening the high-resolution composite image using the posterior surface;

estimating one or more biologically-defined micro-layers of the cornea from the flattening using the anterior surface;

estimating one or more the biologically-defined micro-layers of the cornea from the flattening using the posterior surface; and

generating a segmented high-resolution composite image by combining the estimating from the flattening using the anterior surface with the estimating from the flattening using the posterior surface.

17. The method of claim 16, wherein estimating one or more biologically-defined micro-layers of the cornea from the flattening using the anterior surface comprises

performing a vertical projection of the flattening using the anterior surface and identifying one or more contrast transition surfaces corresponding to interfaces between the one or more biologically-defined micro-layers;

and

wherein estimating one or more biologically-defined micro-layers of the cornea from the flattening using the posterior surface comprises performing a vertical projection of the flattening using the posterior surface and identifying one or more contrast transition surfaces corresponding to interfaces between the one or more biologically-defined micro-layers.

18. The method of claim 17, wherein the one or more contrast transition surfaces corresponding to an anterior interface of the epithelium, an epithelium/basal epithelial layer interface, a basal epithelium/Bowman'"'"'s interface, Bowman'"'"'s/stroma interface, an anterior interface of the endothelial/Descemet'"'"'s layers, a posterior interface of the endothelial/Descemet'"'"'s layers, an aqueous humor interface, and/or a collagen crosslinking layer interface.

19. A system comprising:

one or more processors;

a computer-readable memory storing non-transient instructions that when executed by the one or more processors cause the system to;

segment, using an image processor, a plurality of high-resolution images of a cornea of the eye, to identify one or more of the plurality of biologically-defined micro-layers of the cornea, the plurality of high-resolution images comprising a plurality of images for a plurality of biologically-defined micro-layers of the cornea, the segmentation causing the system to;

(a) perform an image registration on the plurality of high-resolution images by (i) identifying an anterior surface of one of the plurality of biologically-defined micro-layers of the cornea, the anterior surface being a registration reference surface, (ii) comparing a plurality of image frames in the plurality of high-resolution images to the anterior surface, (iii) extracting frames that do not satisfy a registration condition, and (iv) for the remaining frames performing a summation and averaging process to produce a high-resolution composite image of the one of the plurality of biologically-defined micro-layers;

(b) identify one or more contrast transition surfaces for the high-resolution composite image, the transition surfaces corresponding to interfaces between the different corneal micro-layers; and

(c) perform (a) for an adjacent biologically-defined micro-layer to produce a second high-resolution composite image, the adjacent biologically-defined micro-layer being identified by the one or more contrast transition surfaces;

determine thickness data for each of the identified one or more of the plurality of biologically-defined micro-layers, from the segmentation of the plurality of high-resolution images;

develop, from the thickness data for each of the identified one or more of the plurality of biologically-defined micro-layers, a thickness map, the thickness map identifying differences in corneal thickness across the identified biologically-defined micro-layer; and

display the thickness map.

20. The system of claim 19, wherein (b) is performed to identify a transition to an anterior interface of the epithelium, an epithelium/basal epithelial layer interface, a basal epithelium/Bowman'"'"'s interface, Bowman'"'"'s/stroma interface, an anterior interface of the endothelial/Descemet'"'"'s layers, an interface of the endothelial/Descemet'"'"'s layers, an aqueous humor, and/or a collagen crosslinking layer interface, wherein (c) is performed for each biologically-defined micro-layer of the cornea.

21. A system comprising:

one or more processors;

a computer-readable memory storing non-transient instructions that when executed by the one or more processors cause the system to;

perform a two-surface registration on each of a plurality of high-resolution images of the cornea, the plurality of high-resolution images comprising a plurality of images for a plurality of biologically-defined micro-layers of the cornea, and generate a high-resolution composite image of the cornea, wherein the two-surface registration comprises an anterior surface registration and a posterior surface registration;

segment the high-resolution composite image to identify each of the plurality of biologically-defined micro-layers of the cornea, wherein segmentation of the high-resolution composite image comprises flattening the high-resolution composite image and performing a vertical projection of a flattened rendition of the high-resolution composite image to produce a segmented high-resolution composite image;

determine the thickness of at least one of the plurality of biologically-defined micro-layers of the cornea from the segmented high-resolution composite image;

develop a thickness map for at least one of the plurality of biologically-defined micro-layers of the cornea, the thickness map identifying visual differences in thickness across the at least one of the plurality of biologically-defined micro-layers; and

display the thickness map.

22. The system of claim 21, wherein the computer-readable memory stores further non-transient instructions that when executed by the one or more processors cause the system to:

(a) perform the image registration on the plurality of high-resolution images by, for each of the plurality of high-resolution images, (i) identifying an anterior surface of the cornea and a posterior surface of the cornea, and (ii) matching the anterior surface of the cornea and the posterior surface of the cornea to an anterior surface and a posterior surface, respectively, of a reference frame; and

(b) perform a summation and averaging process to produce a high-resolution composite image from the registered plurality of high-resolution images, where the high-resolution composite image is of the cornea.

23. The system of claim 22, wherein the computer-readable memory stores further non-transient instructions that when executed by the one or more processors cause the system to:

perform the image registration on the plurality of high-resolution images by identifying an apex of the cornea and matching the apex of the cornea to an apex of a reference frame.

24. A system comprising:

one or more processors;

a computer-readable memory storing non-transient instructions that when executed by the one or more processors cause the system to;

perform an image registration on a plurality of high-resolution images of a cornea to generate a high-resolution composite image of the cornea from a plurality of high-resolution images of the cornea, the plurality of high-resolution images comprising a plurality of images for a plurality of biologically-defined micro-layers of the cornea;

segment the high-resolution composite image to identify each of the plurality of biologically-defined micro-layers of the cornea using a multiple surface flattening on the high-resolution composite image, the segmentation generating a segmented high-resolution composite image;

determine the thickness of at least one of the plurality of biologically-defined micro-layers of the cornea from the segmented high-resolution composite image;

develop a thickness map for the at least one of the plurality of biologically-defined micro-layers of the cornea, the thickness map identifying visual differences in thickness across the at least one of the plurality of biologically-defined micro-layers; and

display the thickness map.

25. The system of claim 24, wherein the non-transient instructions that when executed by the one or more processors cause the system to segment the high-resolution composite image to identify each of the plurality of biologically-defined micro-layers of the cornea using a multiple surface flattening on the high-resolution composite image, cause the system to:

identify an anterior surface and a posterior surface of the high-resolution composite image;

flatten the high-resolution composite image using the anterior surface;

flatten the high-resolution composite image using the posterior surface;

estimate one or more biologically-defined micro-layers of the cornea from the flattening using the anterior surface;

estimate one or more biologically-defined micro-layers of the cornea from the flattening using the posterior surface; and

generate a segmented high-resolution composite image by combining the estimating from the flattening using the anterior surface with the estimating from the flattening using the posterior surface.

26. The system of claim 25, wherein the computer-readable memory stores further non-transient instructions that when executed by the one or more processors cause the system to:

estimate the one or more biologically-defined micro-layers of the cornea from the flattening using the anterior surface by performing a vertical projection of the flattening using the anterior surface and identifying one or more contrast transition surfaces corresponding to interfaces between the one or more biologically-defined micro-layers; and

estimate the one or more biologically-defined micro-layers of the cornea from the flattening using the posterior surface by performing a vertical projection of the flattening using the posterior surface and identifying one or more contrast transition surfaces corresponding to interfaces between the one or more biologically-defined micro-layers.

27. The system of claim 24, wherein the image registration is a multiple surface registration, and wherein the plurality of high-resolution images comprise images of the cornea, each being curved images with an apex.