COMPUTER-AIDED DIAGNOSIS OF RETINAL PATHOLOGIES USING FRONTAL EN-FACE VIEWS OF OPTICAL COHERENCE TOMOGRAPHY

• US 20120194783A1
• Filed: 01/27/2012
• Published: 08/02/2012
• Est. Priority Date: 01/28/2011
• Status: Abandoned Application

• Current Assignee: Optovue Incorporated
• Original Assignee: Optovue Incorporated
• Inventors: Wei, Jay, Jang, Ben, Davis, John, Lumbroso, Bruno
• Application Number:
  US13/360,503
• Publication Number:
  US 20120194783A1
• Time in Patent Office: Days
• US Class Current: 351/206
• 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 7/0012 Biomedical image inspection

Abstract

A system and methods of computer-aided diagnosis for ophthalmology are described that includes acquiring OCT data, determining an RPE fit from the OCT data, and displaying en face images based on the RPE fit.

Claims

1. A method of computer-aided diagnosis for ophthalmology, comprising:

acquiring an OCT dataset;

obtaining a segmented layer of interest from the OCT dataset;

generating a set of frontal en-face images based on the segmented layer of interest; and

displaying the set of frontal en-face images,

wherein the frontal en-face images are suitable for qualitative and quantitative assessment of a retina.

2. The method of claim 1, further including processing the OCT dataset for noise suppression.

3. The method of claim 1, further including processing the OCT dataset for contrast enhancement.

4. The method of claim 1, wherein obtaining a segmented layer of interest includes determining an RPE fit, an ILM layer, or an RPE layer.

5. The method of claim 1, wherein the qualitative assessment includes structural and morphological assessment on at least one area of interests.

6. The method of claim 5, wherein the structural assessment includes computation of metrics, including at least one of a set of metrics consisting of intensity, homogeneity, boundary thickness, smoothness, connectedness of the area of interest.

7. The method of claim 5, wherein the morphological assessment includes computation of metrics, including one of shape, size, and regularity of the area of interest.

8. The method of claim 7, wherein the area of interest includes a retina, a choroid, an interface of vitreous-retina, a retina-choroid, and a choroid-sclera.

9. The method of claim 7, wherein the morphological assessment includes examination of the shape and dimensions of retina and choroid, as well as the interfaces of vitreous-retina, retina-choroid, and choroid-sclera.

10. The method of claim 5, wherein RPE structure and morphology provide for early detection of macular diseases.

11. The method of claim 10, wherein macular diseases includes drusen, geographic atrophy, and pigment epithelium detachments.

12. The method of claim 8, wherein choroidal vascular changes provide detection of choroidal melanomas.

13. The method of claim 8, wherein choroidal layer thickness and volume provide detection of choroidal neovascularization and age related macular degeneration.

14. The method of claim 1, wherein the set of en-face images includes a plurality of images based on the segmented layer of interest and a B-scan image and displaying the set of en-face images includes simultaneously displaying the set of en-face images on a single display.

15. The method of claim 14, wherein the set of en-face images includes a vitreo reintal interface image, an edema image, a retinal degeneration image, a choroidal image, and a cross-sectional image of a B-scan.

16. An OCT imaging system, comprising:

an OCT imager that acquires OCT data;

a computer coupled to the OCT imager and a display, the computer executing instructions for;

obtaining an RPE fit from the OCT dataset;

generating a set of frontal en-face images based on the RPE fit; and

displaying the set of frontal en-face images

wherein the frontal en-face images are suitable for qualitative and quantitative assessment of a retina.

17. The system of claim 16, further including processing the OCT dataset for noise suppression.

18. The system of claim 16, further including processing the OCT dataset for contrast enhancement.

19. The system of claim 16, wherein obtaining an RPE fit from the OCT database includes determining the curvature of the RPE.

20. The method of claim 16, wherein the set of en-face images includes a plurality of images based on the segmented layer of interest and a B-scan image and displaying the set of en-face images includes simultaneously displaying the set of en-face images on the display.

21. The method of claim 20, wherein the set of en-face images includes a vitreo reintal interface image, an edema image, a retinal degeneration image, a choroidal image, and a cross-sectional image of a B-scan.