System And Method For Detecting Retina Disease
Abstract
A system and method for diagnosing retina disease is disclosed. The method comprises capturing a plurality of images of the vascular network within the retina, such as through the use of optical coherence tomography (OCT). This plurality of images are then processed to determine the location and diameter of each vessel in the three-dimensional vascular network in the retina. The vascular network is then divided into a plurality of equal unit volumes. The vessel density, vascular volume density and other metrics can then be determined for each unit volume. This information can then be used to identify retina disease. The information can be parsed and presented in a variety of ways.
Claims
exact text as granted — not AI-modified1 . A method of diagnosing disease in a retina, comprising:
collecting a plurality of images of the retina; processing the plurality of images to create a 3D computer model, where blood vessels are modelled as a series of stacked disks; dividing the 3D model into a plurality of equally sized volumes; determining a vascular density in each equally sized volume based on a number of disks in each equally sized volume; and analyzing the vascular density in at least a portion of the equally sized volumes to determine the presence of a disease.
2 . The method of claim 1 , wherein the analyzing comprises determining a mean vascular density and comparing the mean vascular density to a predetermined threshold.
3 . The method of claim 1 , wherein the analyzing comprises creating a histogram of vascular density vs. percentage of equally sized volumes.
4 . The method of claim 3 , wherein the analyzing further comprises comparing a shape of the histogram to a predetermined curve.
5 . The method of claim 3 , wherein the analyzing further comprises establishing a first threshold value and identifying the equally sized volumes that have a vascular density greater than the first threshold value as hot regions.
6 . The method of claim 5 , wherein the analyzing further comprises determining a percentage of equally sized volumes that are in the hot region and comparing the percentage to a predetermined threshold.
7 - 13 . (canceled)
14 . A method of detecting progression of a disease in a retina of a patient, comprising:
collecting a first plurality of images of the retina of the patient at a first point in time; processing the first plurality of images to create a first 3D computer model, where blood vessels are modelled as a series of stacked disks; dividing the first 3D model into a plurality of equally sized volumes; determining a first vascular density in each equally sized volume based on a number of disks in each equally sized volume; collecting a second plurality of images of the retina of the patient at a second point in time; processing the second plurality of images to create a second 3D computer model, where blood vessels are modelled as a series of stacked disks; dividing the second 3D model into the plurality of equally sized volumes; determining a second vascular density in each equally sized volume based on a number of disks in each equally sized volume; analyzing the first vascular density and the second vascular density to determine progression of a disease.
15 . The method of claim 14 , wherein the analyzing comprises determining a first mean vascular density for the first 3D model and a second mean vascular density for the second 3D model, and comparing the first mean vascular density to the second mean vascular density.
16 . The method of claim 15 , wherein the second mean vascular density is adjusted to account for the change in an age of the patient.
17 . The method of claim 14 , further comprising creating a first histogram of vascular density vs. percentage of equally sized volumes for the first 3D model and a second histogram of vascular density vs. percentage of equally sized volumes for the second 3D model.
18 . The method of claim 17 , wherein the analyzing further comprises:
establishing a first threshold value; identifying the equally sized volumes in the first histogram and the second histogram that had a vascular density greater than the first threshold value as hot regions; determining a first percentage of equally sized volumes that are in the hot region for each histogram; and
comparing the first percentage in the first histogram to the first percentage in the second histogram.
19 . The method of claim 18 , wherein the analyzing further comprises:
establishing a second threshold value; identifying the equally sized volumes in the first histogram and the second histogram that had a vascular density less than the second threshold value as cold regions; determining a second percentage of equally sized volumes that are in the cold region for each histogram; and comparing the second percentage in the first histogram to the second percentage in the second histogram.
20 . The method of claim 18 , wherein the first threshold value is different for the first 3D model and the second 3D model to account for a change in an age of the patient.
21 . The method of claim 14 , wherein the portion of equally sized volumes comprises all equally sized volumes that are disposed in one plane.
22 . The method of claim 14 , wherein the retina is divided into a plurality of annular rings, and the portion of equally sized volumes comprises all equally sized volumes that are disposed in at least one of the annular rings.
23 . The method of claim 14 , wherein the retina is divided into a plurality of wedges, and the portion of equally sized volumes comprises all equally sized volumes that are disposed in at least one of the wedges.
24 . The method of claim 22 , wherein the retina is also divided into a plurality of wedges, and the portion of equally sized volumes comprises all equally sized volumes that are disposed in at least one of the annular rings and disposed in at least one of the wedges.Cited by (0)
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