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Delia Cabrera DeBuc, Ph.D.

Delia Cabrera DeBuc, Ph.D.

Delia Cabrera DeBuc, Ph.D.

Research Subject

Quantitative Ophthalmic Imaging and Artificial Intelligence Applications


Diabetic Retinopathy and neurodegenerative diseases with complications in the eye such as Alzheimer’s Disease, Parkinson Disease, and Multiple Sclerosis.

Published Articles


Research Associate Professor of Ophthalmology


Dr. Cabrera DeBuc’s laboratory research focuses on developing methods and algorithms to quantify pathological features and treatment-induced changes in patients with ocular and neurological diseases. The primary focus is to develop quantitative tools to improve ocular imaging and image processing analysis for clinical use as well as to identify novel imaging biomarkers of the onset and progression of ophthalmic and neurological diseases using advanced optical imaging (e.g., OCTA, LSFG, RFI, cSLO). The lab also has an interest in developing and translating low-cost multimodal approaches for eye screening integrated with telemedicine and artificial intelligence applications in primary care and community settings.

Current Research

1. Seeing the Brain through the Eye: Multimodal Diagnostic Eye Biomarkers of Cognitive Impairment
Aside from ophthalmic-image processing research, our group is also actively involved in the identification of novel ocular imaging biomarkers in neurodegenerative diseases of the central nervous system such as multiple sclerosis, Alzheimer and Parkinson’s diseases. Particularly, preliminary results from our most recent research on Alzheimer’s disease (AD) sponsored by the Finker Frenkel Legacy Foundation and the Alzheimer’s Association, is revealing that potential biomarkers of mild cognitive impairment could be identified by assessing their retinal vascular complexity and neurodegenerative changes with low-cost ophthalmic technologies (Fig.1). Our results also add support to the use of a multimodal diagnostic biomarker approach of cognitive impairment based on the retinal structure-function relationship which also has the advantage of requiring a low-cost implementation that can be used in community settings to detect cognitive decline-specific pathology in the retina, which could enable the early diagnosis and monitoring of disease progression. Provided a clinical correlation between the eye and brain measures can be confirmed, screening of eyes in people being considered at risk of cognitive impairment could help in the development of an alternative low-cost approach for early diagnosis as well as potentially serve to monitor the effectiveness of emerging therapies.

Normal Macular Histology vs. OCT
Figure 1. Retinal topographical features observed in individuals with mild cognitive impairment.Top row: Central and nasal infrared light-images obtained from a female subject (79 years old) with MCI showing extramacular features such as drusen-like regions depicted by irregularly shaped bright spots in the periphery of the superior quadrant as well as with pigment dispersion in both eyes. Bottom image: Left- Central and nasal infrared light-images obtained from a female subject (81 years old) with MCI showing tortuous vessels, extramacular features such as drusen-like regions along with pigment dispersion in the left eye. Right- Nasal infrared-light image obtained from a healthy control (71 years old). All images were acquired with the EasyScan Unit (i-Optics Corporation, The Netherlands). The EasyScan camera is a dual color confocal SLO: Infrared (785 nm) and pure green (532 nm). The different colors are related to a different penetration depth. The red arrows indicate the location of the drusen and white spots observed at extramacular locations. The ROIs enclosed by the orange rectangles indicate the locations where pigment dispersion was observed. The green light-image is reflected at the retinal nerve fiber layer showing the vascular structure up to the 4th bifurcation. The infrared light-image is reaching the choroidal vessel layer.

2. Quantitative assessment of early changes of diabetic retinal pathology
Another primary focus of our lab is to study the retinal microvasculature and structure using advanced optical imaging technologies. As a result, we have developed a computer-assisted diagnosis method for quantitative analysis of OCT images (OCTRIMA3D) from patients with diabetes mellitus and healthy subjects (Fig.2).

Our early work demonstrated that local changes in the diabetic retina could be used as surrogates for the subsequent progress of retinopathy inducing visual defects. For example, our initial research sponsored by the Juvenile Diabetes Research Foundation has demonstrated that changes in the ganglion cell complex might be the first indication of retinal neurodegeneration in patients with diabetes (cross-sectional studies). Moreover, we have introduced a methodological framework based on feature segmentation, machine learning and chaos theory for analyzing and classifying anatomical or pathological features of interest in ocular images captured with advanced optical imaging modalities. Also, ongoing research in the laboratory, initially funded by an NIH/NEI award, is currently addressing whether retinal neurodegeneration may be a primary pathology that gives rise to microvascular changes in the diabetic eye. Our long-term goal is to improve the early diagnosis and treatment of diabetic retinopathy.

OCT image of a healthy macula after processing with OCTRIMA
Figure.2. OCTRIMA3D algorithm. A) Histogram of boundary detection errors as compared to inter-observer differences. The average unsigned errors are shown below each histogram. B) Left: Parafoveal OCT B-scan (Spectralis SD-OCT) showing eight intraretinal layer boundaries segmented. Note that red, yellow, magenta, white, cyan, green, black and blue boundaries are delineated using solid lines (notations in table at right).

3. Advanced imaging for diabetic retinopathy (AIDR) study
Advances in preventing vision loss in diabetic patients are hindered by limited understanding of mechanisms underlying diabetic retinopathy (DR) and the altered relationships between the retinal neural tissue and retinal vasculature. Therefore, an objective test for the early diagnosis and evaluation of DR treatment is certainly needed in order to identify the individuals at great risk for vision-threatening problems. Studies have shown that, in DR, retinal neurodegeneration may be a primary pathology that gives rise to microvascular changes. While most studies have tried to explain the visual dysfunction in DR by focusing on the retinal vasculature changes, the potential contribution of photoreceptor cells which account for most of the mass and metabolic activity of the retina has been largely ignored.

We are studying the retinal microvasculature and structure using advanced optical imaging technologies. Our most recent results (longitudinal studies) have revealed that the outer retina may play an essential role in the neurodegeneration observed at the early stage of retinopathy even before the ganglion cells are lost (Figs. 3, 4 & 5). Our long-term goal is to prevent visual loss in diabetic patients based on a better understanding of the underlying mechanisms and the altered relationships between the neural retina and blood vessels.

OCT image of a healthy macula after processing with OCTRIMA
Figure. 3. Photoreceptor degeneration- human postmortem samples. The sections were reacted with a combination of antibodies labeling mitochondria (MTCO-2 in red), M- and L-opsins (AB5405 in white) and rhodopsin (AO in green). DAPI (in blue) is used for nuclear staining. This combination delineates all retinal sublayers and clearly labels cone (upper row – in white) and rod outer segments (lower row – in green). Whereas in controls outer segment morphology is normal for both receptor types (a, d - from patient #330), in diabetes normal morphology can only be detected in early diabetic patients only (b, e – from patient #332 with less than 1 year of diabetes) while in the other cases/patients the outer segment and general morphology is inferior to those seen in controls in most regions (c, f – from patient #343 with more than 10 years of diabetes). RPE: retinal pigmented epithelium, OS: outer segments, E: ellipsoid, M: myoid, ONL: outer nuclear layer, HFL: Henle fiber layer, OPL: outer plexiform layer. Bar: 20 µm.

OCT image of a healthy macula after processing with OCTRIMA
Figure. 4. Fovea morphology (Top: subject #330 (control). Bottom: subject #331 (DM))

OCT image of a healthy macula after processing with OCTRIMA
Fig. 5. The changes of layer thickness during the three-year follow-up in the two groups with diabetes according to the ETDRS subfields. Color codes are showing significant changes as indicated by linear regression. For the color codes see the bottom of the Figure. Left ETDRS map, DM group, and Right ETDRS map, MDR group. Abbreviations: DM, eyes without DR (n=55); MDR, eyes with mild non-proliferative DR (n=22); RNFL, retinal nerve fiber layer; GCL+IPL, ganglion cell layer and inner plexiform layer complex; INL, inner nuclear layer; ONL, outer nuclear layer; MZ, myoid zone; ELZ, ellipsoide zone; OS, outer segment; OPL, outer plexiform layer; RPE/BC, retinal pigment epithelium/Bruch’s complex. C, central subfield, S1/S2, inner/outer superior; N1/N2, inner/outer nasal; I1/I2, inner/outer inferior; T1/T2, inner/outer temporal.)