scholarly journals Automated identification of cone photoreceptors in adaptive optics optical coherence tomography images using transfer learning

2018 ◽  
Vol 9 (11) ◽  
pp. 5353 ◽  
Author(s):  
Morgan Heisler ◽  
Myeong Jin Ju ◽  
Mahadev Bhalla ◽  
Nathan Schuck ◽  
Arman Athwal ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Transfer Learning ◽  
Adaptive Optics ◽  
Optical Coherence ◽  
Cone Photoreceptors ◽  
Automated Identification

scholarly journals High-speed volumetric imaging of cone photoreceptors with adaptive optics spectral-domain optical coherence tomography

2006 ◽  
Vol 14 (10) ◽  
pp. 4380 ◽  
Author(s):  
Yan Zhang ◽  
Barry Cense ◽  
Jungtae Rha ◽  
Ravi S. Jonnal ◽  
Weihua Gao ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Adaptive Optics ◽  
High Speed ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Cone Photoreceptors ◽  
Volumetric Imaging

scholarly journals Imaging cone photoreceptors in three dimensions and in time using ultrahigh resolution optical coherence tomography with adaptive optics

2011 ◽  
Vol 2 (4) ◽  
pp. 748 ◽  
Author(s):  
Omer P. Kocaoglu ◽  
Sangyeol Lee ◽  
Ravi S. Jonnal ◽  
Qiang Wang ◽  
Ashley E. Herde ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Adaptive Optics ◽  
Three Dimensions ◽  
Optical Coherence ◽  
Cone Photoreceptors ◽  
Ultrahigh Resolution

scholarly journals Three-dimensional composition of the photoreceptor cone layers in healthy eyes using adaptive-optics optical coherence tomography (AO-OCT)

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245293
Author(s):  
Adrian Reumueller ◽  
Lorenz Wassermann ◽  
Matthias Salas ◽  
Markus Schranz ◽  
Valentin Hacker ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Adaptive Optics ◽  
Outer Segment ◽  
Three Dimensional ◽  
Optical Coherence ◽  
Cone Photoreceptors ◽  
Cone Mosaic ◽  
Automated Technique ◽  
Healthy Eyes ◽  
Cone Density

Purpose To assess the signal composition of cone photoreceptors three-dimensionally in healthy retinas using adaptive optics optical coherence tomography (AO-OCT). Methods Study population. Twenty healthy eyes of ten subjects (age 23 to 67). Procedures. After routine ophthalmological assessments, eyes were examined using AO-OCT. Three-dimensional volumes were acquired at 2.5° and 6.5° foveal eccentricity in four main meridians (superior, nasal, inferior, temporal). Cone densities and signal compositions were investigated in four different planes: the cone inner segment outer segment junction (IS/OS), the cone outer segment combined with the IS/OS (ISOS+), the cone outer segment tips (COST) and full en-face plane (FEF) combining signals from all mentioned cone layers. Additionally, reliability of a simple semi-automated approach for assessment of cone density was tested. Main outcome measures. Cone density of IS/OS, IS/OS+, COST and FEF. Qualitative depiction and composition of each cone layer. Inter-rater agreement of cone density measurements. Results Mean overall cone density at all eccentricities was highest at the FEF plane (21.160/mm2), followed by COST (20.450/mm2), IS/OS+ (19.920/mm2) and IS/OS (19.530/mm2). The different meridians and eccentricities had a significant impact on cone density, with lower eccentricity resulting in higher cone densities (p≤.001), which were highest at the nasal, then temporal, then inferior and then superior meridian. Depiction of the cone mosaic differed between all 4 layers regarding signal size and packing density. Therefore, different cone layers showed evident but not complete signal overlap. Using the semi-automated technique for counting of cone signals achieved high inter-rater reliability (ICC > .99). Conclusions In healthy individuals qualitative and quantitative changes in cone signals are found not only in different eccentricities and meridians, but also within different photoreceptor layers. The variation between cone planes has to be considered when assessing the integrity of cone photoreceptors in healthy and diseased eyes using adaptive optics technology.

Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images

2019 ◽  
Vol 198 ◽  
pp. 136-145 ◽  
Author(s):  
Ryo Asaoka ◽  
Hiroshi Murata ◽  
Kazunori Hirasawa ◽  
Yuri Fujino ◽  
Masato Matsuura ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Transfer Learning ◽  
Early Onset ◽  
Optical Coherence

Optical Coherence Tomography and Glaucoma

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Alexi Geevarghese ◽  
Gadi Wollstein ◽  
Hiroshi Ishikawa ◽  
Joel S. Schuman
Keyword(s):  
Optical Coherence Tomography ◽  
Adaptive Optics ◽  
Imaging Modality ◽  
Annual Review ◽  
Publication Date ◽  
Optical Coherence ◽  
Swept Source ◽  
Diagnosis And Management ◽  
Spectral Domain Oct ◽  
Glaucoma Diagnosis

Early detection and monitoring are critical to the diagnosis and management of glaucoma, a progressive optic neuropathy that causes irreversible blindness. Optical coherence tomography (OCT) has become a commonly utilized imaging modality that aids in the detection and monitoring of structural glaucomatous damage. Since its inception in 1991, OCT has progressed through multiple iterations, from time-domain OCT, to spectral-domain OCT, to swept-source OCT, all of which have progressively improved the resolution and speed of scans. Even newer technological advancements and OCT applications, such as adaptive optics, visible-light OCT, and OCT-angiography, have enriched the use of OCT in the evaluation of glaucoma. This article reviews current commercial and state-of-the-art OCT technologies and analytic techniques in the context of their utility for glaucoma diagnosis and management, as well as promising future directions. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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