|Year : 2020 | Volume
| Issue : 10 | Page : 2251
High-contrast scleroconjunctival microvasculature via deep learning denoising
Shin Kadomoto, Akihito Uji, Yuki Muraoka, Akitaka Tsujikawa
Department of Ophthalmology, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, Japan
|Date of Web Publication||23-Sep-2020|
Dr. Akitaka Tsujikawa
Department of Ophthalmology, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto 606-8507
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Kadomoto S, Uji A, Muraoka Y, Tsujikawa A. High-contrast scleroconjunctival microvasculature via deep learning denoising. Indian J Ophthalmol 2020;68:2251
|How to cite this URL:|
Kadomoto S, Uji A, Muraoka Y, Tsujikawa A. High-contrast scleroconjunctival microvasculature via deep learning denoising. Indian J Ophthalmol [serial online] 2020 [cited 2020 Oct 31];68:2251. Available from: https://www.ijo.in/text.asp?2020/68/10/2251/295656
Anterior segment optical coherence tomography angiography (AS-OCTA) is a newly developed technique for noninvasive imaging of blood flow, including the scleroconjunctival microvasculature and iris., However, AS-OCTA images frequently have background noise because the OCTA devices were developed specially for retinal imaging. The left eye of a 45-year-old healthy individual of AS-OCTA image [Figure 1]a provides a scleroconjunctival microvasculature with significant background noise. A high-contrast image [Figure 1]b and [Figure 1]c is produced by applying deep learning-based denoising method (Intelligent denoise, Canon, Inc, Tokyo, Japan). Panoramic high-contrast AS-OCTA image [Figure 1]c processed with deep learning-based denoising is useful to evaluate circumferential conjunctival vasculature.
|Figure 1: Swept-source anterior segment-optical coherence tomography angiography (AS-OCTA) images (OCT S1: Canon, Inc, Tokyo, Japan). To visualize the conjunctival and deep scleral vessels, a 400-μm thick slab of the conjunctival surface was selected. Seven AS-OCTA images were used to reconstruct a panoramic image.|
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Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
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Akagi T, Uji A, Huang AS, Weinreb RN, Yamada T, Miyata M, et al
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Kadomoto S, Uji A, Muraoka Y, Akagi T, Tsujikawa A. Enhanced visualization of retinal microvasculature in optical coherence tomography angiography imaging via deep learning. J Clin Med2020;9. doi: 10.3390/jcm9051322.