activity
20202026
most citedAutomated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning

59 citations · 126 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2026

Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography

Yukun Guo, Min Gao, Tristan T. Hormel +3

Optical coherence tomographic angiography (OCTA) is a powerful technique for imaging retinal microvasculature. However, acquiring reliable quantification of retinal blood flow and…

q-bio.QM2025

AI-derived layer-specific OCT biomarkers for classification of geographic atrophy

Yukun Guo, An-Lun Wu, Tristan T. Hormel +5

Geographic atrophy (GA) is a key biomarker of dry age-related macular degeneration (AMD) traditionally identified through color fundus photography. Hyper-transmission defects (hype…

q-bio.QM2024

Nonperfused Retinal Capillaries -- A New Method Developed on OCT and OCTA

Min Gao, Yukun Guo, Tristan T. Hormel +6

To develop a new method to quantify nonperfused retinal capillaries (NPCs) by using co-registered optical coherence tomography (OCT) and OCT angiography (OCTA), and to evaluate NPC…

q-bio.QM2023★ 23 cited

Perfused and Nonperfused Microaneurysms Identified and Characterized by Structural and Angiographic OCT

Min Gao, Tristan T. Hormel, Yukun Guo +5

Purpose: Microaneurysms (MAs) have distinct, oval-shaped, hyperreflective walls on structural OCT, and inconsistent flow signal in the lumen with OCT angiography (OCTA). Their rela…

eess.IV2022★ 3 cited

Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map

Pengxiao Zang, Tristan T. Hormel, Jie Wang +6

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCT…

eess.IV2020★ 59 cited

Automated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning

Yukun Guo, Tristan T. Hormel, Honglian Xiong +3

Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomograp…

eess.IV2020★ 3 cited

DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography

Pengxiao Zang, Liqin Gao, Tristan T. Hormel +4

Objective: Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages for the early detection and diagnosis of diabetic retinopathy (DR). However, automa…

physics.med-ph2020★ 36 cited

High-resolution wide-field OCT angiography with a self-navigation method to correct microsaccades and blinks

Xiang Wei, Tristan T. Hormel, Yukun Guo +2

In this study, we demonstrate a novel self-navigated motion correction method that suppresses eye motion and blinking artifacts on wide-field optical coherence tomographic angiogra…

eess.IV2020★ 2 cited

Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learning

Min Gao, Yukun Guo, Tristan T. Hormel +3

Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3x3- or 6x6-mm. Compared to 3x3-mm angiograms with proper sampling density, 6x6…