Purpose.
To create and validate software to automatically segment leakage area in real-world clinical fluorescein angiography (FA) images of subjects with diabetic macular edema (DME).

/pdf/fully-automatic-segmentation-of-fluorescein-leakage-in-1ky20m5wly.pdf

Fully automatic segmentation of fluorescein leakage in subjects with diabetic macular edema.

The retinal vasculature is a complex, layered network of vessels and capillary beds that permeate the retina. They provide a steady supply of nutrients and dispose of cellular byproducts, both essential for maintaining retinal health and function. Compromise of this support system has severe consequences to the retina and often underlies retinal abnormalities, in particular diabetic retinopathy, a leading cause of blindness in the Western world. Fluorescein angiography (FA)1–7 and entoptic viewing8–15 are established methods for observing fine details of the retinal vasculature and early pathologic changes. Both techniques, however, are not without shortcomings. FA requires injection of a toxic fluorescent dye and fails to detect a significant fraction of capillaries, especially those with small diameters, because of its poor depth sectioning capability and sensitivity to quality of the angiogram (optical quality of the eye). In spite of these shortcomings, FA has been used to measure the retinal microcirculation (e.g., capillary blood flow velocity5–7), area between capillaries,6 and FAZ size.1–4,6

Entoptic viewing is a psychophysical method that allows self-visualization of the retinal vasculature or, more specifically, the shadow patterns the vasculature casts during illumination of the retina. Unlike FA, entoptic viewing is noninvasive, unaffected by the eye's optics, and limited to the fovea for viewing capillaries. In this localized area, the method has been used to study foveal capillary details, FAZ size and shape,8–10 foveal vessels,9–11 macular blood flow,12,13 and capillary density around the FAZ.14 It has also been compared with fluorescein angiography and shown to contain more foveal capillary detail.15

More recently, retinal imaging instruments equipped with adaptive optics (AO) have garnered considerable interest for imaging the retinal microvasculature. AO increases imaging resolution and sensitivity, both advantageous for detecting small, weakly reflecting capillaries. AO scanning laser ophthalmoscopes (AO-SLOs) have been used to map the local 2D distribution and flow dynamics of the retinal microvasculature in living eyes.16–18 AO retina cameras based on flood illumination have also demonstrated similar imaging capability.19 AO has been incorporated into optical coherence tomography (OCT).20–27 The major advantage of OCT is its substantially higher axial resolution28,29 (∼3–8 μm compared with >60 μm for AO-SLO and AO flood-illumination retina cameras), which, when combined with AO, offers the potential to map the microvasculature in all three dimensions to differentiate, for example, the multilaminar networks of capillaries.

There have been reports of AO-OCT imaging of retinal capillaries,23–25,30–32 in particular that by Hammer et al.,24 who compared the diameters of capillaries of patients with retinopathy of prematurity with those of persons without it. The vasculature detail realized with these instruments is impressive, yet the qualitative nature of these observations along with the quantitative analysis of Hammer et al.24 do not address whether the capillaries are faithfully imaged. This raises a fundamental issue about the capacity of AO-OCT to image capillaries, whose size varies considerably (2.5–7 μm33; 2.5–10 μm34) and that network with vessels of increasingly larger size. Certainly the AO-SLO mapping of the small capillaries near the rim of the FAZ suggests AO-OCT is capable of the same, but this has yet to be demonstrated. Moreover, AO-SLO and AO-OCT instruments are fundamentally different. The interferometric nature of AO-OCT results in high-contrast speckle that permeates the AO-OCT image and confounds the detection of structures that approach the size of speckle (e.g., comparable to the diameters of small capillaries). The much slower image acquisition of AO-OCT compared with AO-SLO (100–1000 times slower) also leads to larger and more disruptive motion artifacts that degrade the image and obstruct visualization of capillaries.

Given these unknowns, we investigated the capability of an ultrahigh-resolution AO OCT system (UHR-AO-OCT) to image capillaries in a 3° × 3° region centered on the fovea. This region35–37 is occupied by the FAZ and the network of capillaries that define its terminal rim. Use of the rim capillaries enabled us to compare the UHR-AO-OCT images with entopic viewing, an independent and established psychophysical method for mapping foveal capillaries8–15 on the same seven subjects. Comparisons were made in terms of presence or absence of a FAZ, gross dimensions of the FAZ, and qualitative characteristics of capillary patterns unique to specific eyes. In addition, UHR-AO-OCT measurements of the capillary diameter and depth variation with retinal eccentricity are reported.

/pdf/imaging-retinal-capillaries-using-ultrahigh-resolution-1b8058feeb.pdf

Imaging retinal capillaries using ultrahigh-resolution optical coherence tomography and adaptive optics.

Diabetic retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina. In this article, a computerised DR grading system, which digitally analyses retinal fundus image, is used to measure foveal avascular zone. A v-fold cross-validation method is applied to the FINDeRS database to evaluate the performance of the DR system. It is shown that the system achieved sensitivity of >84%, specificity of >97% and accuracy of >95% for all DR stages. At high values of sensitivity (>95%), specificity (>97%) and accuracy (>98%) obtained for No DR and severe NPDR/PDR stages, the computerised DR grading system is suitable for early detection of DR and for effective treatment of severe cases.

Analysis of retinal fundus images for grading of diabetic retinopathy severity

Determination of foveal avascular zone in diabetic retinopathy digital fundus images

Data from medical imaging system need to be analysed for diagnostics and clinical purposes. In a computerized system, the analysis normally involves classification process to determine disease and its condition. In an earlier work based on a database of 315 fundus images (FINDeRS), it is found that the foveal avascular zone (FAZ) enlargement strongly correlates with diabetic retinopathy (DR) progression having a correlation factor up to 0.883 at significant levels better than 0.01. However, it is also found that the FAZ areas can belong to different DR severity but with different levels of certainty having a Gaussian distribution. In this research work, the suitability of the Gaussian Bayes classifier in determining DR severity level is investigated. A v-fold cross-validation (VFCF) process is applied to the FINDeRS database to evaluate the performance of the classifier. It is shown that the classifier achieved sensitivity of >84%, specificity of >97% and accuracy of >95% for all DR stages. At high values of sensitivity (>95%), specificity (>97%) and accuracy (>98%) obtained for No DR and Severe NPDR/PDR stages, the Gaussian Bayes classifier is suitable as part of a computerised DR grading and monitoring system for early detection of DR and for effective treatment of severe cases.

Gaussian Bayes classifier for medical diagnosis and grading: Application to diabetic retinopathy

Background: The foveal avascular zone (FAZ) is known to enlarge in diabetic retinopathy. In a preliminary study, the authors applied a region growing algorithm to fluorescein angiograms to detect the FAZ in a semi-automated fashion.



Methods:  The FAZ in 44 fluorescein angiograms of 44 eyes of 41 patients with diabetic retinopathy underwent manual outlining, then analysis with the region growing function of the ENVI image analysis software. The same algorithm was applied after median filtering of the images.



Results:  Correlation coefficient was 0.98 between the first two authors, 0.89 between the first author and semi-automated detection before median filtering and 0.91 after median filtering. Average surface areas however, were smaller with semi-automated detection (1951 pixels before and 2226 pixels after median filtering) than with manual detection (3032 pixels).



Conclusions:  Semi-automated detection of the FAZ is possible, but refinements need to be made in angiogram quality and/or image pretreatment to improve results.

Semi-automated detection of the foveal avascular zone in fluorescein angiograms in diabetes mellitus.

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Semi-automated detection of the foveal avascular zone in fluorescein angiograms in diabetes mellitus.