3D RETINAL DISRUPTIONS DETECTION USING OPTICAL COHERENCE TOMOGRAPHY

• US 20120127427A1
• Filed: 11/17/2011
• Published: 05/24/2012
• Est. Priority Date: 11/17/2010
• Status: Active Grant

• Current Assignee: Optovue Incorporated
• Original Assignee: Optovue Incorporated
• Inventors: Jang, Ben, Guo, Cheng-en, Li, Wenjing
• Granted Patent: US 8,622,548 B2
• Time in Patent Office: Days
• US Class Current: 351/206
• CPC Class Codes: A61B 3/102 for optical coherence tomography [OCT]
• CPC Class Codes: G06T 2207/10101 Optical tomography; Optical coherence tomography [OCT]
• CPC Class Codes: G06T 2207/20156 Automatic seed setting
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/187 involving region growing; involving region merging; involving connected component labelling

Abstract

System and method for 3D retinal disruption/elevation detection, measurement and presentation using Optical Coherence Tomography (OCT) are provided. The present invention is capable of detecting and measuring the abnormal changes of retinal layers (retinal disruptions), caused by retinal diseases, such as hard drusen, soft drusen, Pigment Epithelium Detachment (PED), Choroidal Neovascularization (CNV), Geographic Atrophy (GA), intra retinal fluid space, and exudates etc. The presentations of the results are provided with quantitative measurements of disruptions in retina and can be used for diagnosis and treatment of retinal diseases.

Claims

1. An imaging apparatus, comprising:

an optical source;

an x-y scanner receiving light from the optical source and directing it onto a sample;

a detector receiving reflected light from the scanner; and

a computer receiving a signal from the detector and executing code stored in a memory, the code providing instructions for

providing a 3D data set containing voxels of a sample,

identifying one or more 3D seeds from the 3D data set,

obtaining characteristics of 3D disruptions based on the 3D seeds;

generating measurements for the 3D disruptions; and

displaying the results of the 3D disruptions.

2. The apparatus of claim 1, wherein identifying one or more 3D seeds from the 3D data set includes:

segmenting at least one retinal layer in the 3D data set to obtain one or more segmented retinal layers;

generating a normal Retinal Pigment Epithelium (RPE) layer from the at least one retinal layer in the 3D data set to obtain a RPE fit surface; and

calculating the distance between points on the one or more segmented retinal layers to points on the RPE fit surface to obtain at least one elevation map.

3. The apparatus of claim 2, wherein identifying one or more 3D seeds from the 3D data set further includes identifying seed candidates by identifying an adaptive seed search image (ASSI).

4. The apparatus of claim 3, wherein the ASSI is an elevation map.

5. The apparatus of claim 2, further including reducing the 3D data set between the one or more segmented retinal layers into a 2D representation to obtain an en face image.

6. The apparatus of claim 5, wherein the en face image is processed by applying image processing filter to obtain an ASSI, the processing filter being chosen from a set of filters consisting of an edge detection filter, a smoothing filter, and the combination thereof.

7. The apparatus of claim 5, wherein an ASSI can be generated by combining information of the elevation map and the en face image.

8. The apparatus of claim 2, wherein identifying one or more 3D seeds from the 3D data set includes a 3D segmentation computation.

9. The apparatus of claim 8, wherein the 3D region growing computation or a 3D volume construction method by interpolating two or more 2D cross-sectional contour images from the 3D dataset.

10. The apparatus of claim 1, wherein generating measurements for the 3D disruptions is interactive.

11. The apparatus of claim 1, wherein displaying the results of the 3D disruptions includes providing plots of one or more 3D disruption characteristics in relation to at least one anatomical features of an eye.

12. The apparatus of claim 1, further including a progression analysis to evaluate and displaying 3D disruption results from 3D data set obtained from different points in time.

13. The apparatus of claim 1, further including a 3D display of 3D disruption clouds overlaying a segmented layer of interest in a 3D display interface.

14. An image processing method, comprising:

acquiring a 3D data set containing voxels of a sample from an Optical Coherent Tomography (OCT) system;

identifying one or more 3D seeds from the 3D data set;

obtaining characteristics of 3D disruptions based on the 3D seeds;

generating measurements for the 3D disruptions; and

displaying results of the 3D disruptions.

15. The method of claim 14, wherein identifying one or more 3D seeds from the 3D data set includes:

segmenting at least one retinal layer in the 3D data set to obtain one or more segmented retinal layers;

generating a normal Retinal Pigment Epithelium (RPE) layer from the at least one retinal layer in the 3D data set to obtain a RPE fit surface; and

calculating the distance between points on the one or more segmented retinal layers to points on the RPE fit surface to obtain at least one elevation map.

16. The method of claim 15, wherein identifying one or more 3D seeds from the 3D data set further includes identifying seed candidates by identifying an adaptive seed search image (ASSI).

17. The method of claim 16, wherein the ASSI is an elevation map.

18. The method of claim 15, further including reducing the 3D data set between the one or more segmented retinal layers into a 2D representation to obtain an en face image.

19. The method of claim 18, wherein the en face image is processed by applying image processing filter to obtain an ASSI, the processing filter being chosen from a set of filters consisting of an edge detection filter, a smoothing filter, and the combination thereof.

20. The method of claim 18, wherein an ASSI can be generated by combining information of the elevation map and the en face image.

21. The method of claim 15, wherein identifying one or more 3D seeds from the 3D data set includes a 3D segmentation computation.

22. The method of claim 21, wherein the 3D region growing computation or a 3D volume construction method by interpolating two or more 2D cross-sectional contour images from the 3D dataset.

23. The method of claim 14, wherein generating measurements for the 3D disruptions is interactive.

24. The method of claim 14, wherein displaying the results of the 3D disruptions includes providing plots of one or more 3D disruption characteristics in relation to at least one anatomical features of an eye.

25. The method of claim 14, further including a progression analysis to evaluate and displaying 3D disruption results from 3D data set obtained from different points in time.

26. The method of claim 14, further including a 3D display of 3D disruption clouds overlaying a segmented layer of interest in a 3D display interface.