Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Hormel, Tristan T."'
Autor:
Zang, Pengxiao, Hormel, Tristan T., Wang, Jie, Guo, Yukun, Bailey, Steven T., Flaxel, Christina J., Huang, David, Hwang, Thomas S., Jia, Yali
Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion
Externí odkaz:
http://arxiv.org/abs/2212.06299
Autor:
Zang, Pengxiao, Gao, Liqin, Hormel, Tristan T., Wang, Jie, You, Qisheng, Hwang, Thomas S., Jia, Yali
Objective: Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages for the early detection and diagnosis of diabetic retinopathy (DR). However, automated, complete DR classification frameworks based on both OCT and OCTA
Externí odkaz:
http://arxiv.org/abs/2006.05480
Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography (OCT) volume. Methods: 3 x 3-mm OCT scans were acquired on one eye
Externí odkaz:
http://arxiv.org/abs/2006.02569
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-mm angiograms have significantly lower scan quality, with reduced sig
Externí odkaz:
http://arxiv.org/abs/2004.08957
Publikováno v:
Biomed. Opt. Express 11, 3234-3245 (2020)
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 angiography (OCTA) without requiring any hardware modification. Highly efficie
Externí odkaz:
http://arxiv.org/abs/2004.04823