Enhanced Framework for Concurrent correction and Segmentation in Retinal Optical Coherence Tomography

Archana, D (2024) Enhanced Framework for Concurrent correction and Segmentation in Retinal Optical Coherence Tomography. In: 2024 International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India.

[thumbnail of Enhanced Framework for Concurrent correction and Segmentation in Retinal Optical Coherence Tomography.pdf] Text
Enhanced Framework for Concurrent correction and Segmentation in Retinal Optical Coherence Tomography.pdf - Published Version

Download (457kB)

Abstract

A revolutionary imaging technique called optical coherence tomography of visible light (VIS-OCT) of the individual uses shorter visible light wavelengths than traditional near-infrared (NIR) light. To more accurately discern stratified retinal layers, it offers microvascular oximetry together with one-micron level axial resolution. Since the allowed illumination power is significantly lower than NIR OCT due to practical limits regarding laser safety and comfort, it might be difficult to generate VIS-OCT images of a high enough quality for further image processing. As a result, denoising VIS-OCT images is a crucial step in the whole workflow for clinical applications involving VIS-OCT. The first retinal image collection is presented in this study from normal eyes obtained using VIS-OCT. We offer a simultaneous self-denoising and segmentation system based on deep learning. Both tasks complement one another inside the same network to increase each other's productivity. Annotation-efficient training is demonstrated by a discernible increase in When the available annotation falls below 25%, the Dice coefficient was 2% higher than with the segmentation-only method.) is accomplished. Additionally, we demonstrated how well the denoising model learned on our dataset could be applied to an alternative scanning method.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: B-scans; De-noising; Near infrared light; OCT images; Retinal layers; Retinal optical coherence tomography; Segmentation; U-net; Visible light; Visible-light wavelengths
Subjects: A Artificial Intelligence and Data Science > Deep Learning
A Artificial Intelligence and Data Science > Cognitive Science
Divisions: Electronics and Communication Engineering
Depositing User: Users 5 not found.
Date Deposited: 27 Jul 2024 11:01
Last Modified: 27 Jul 2024 11:01
URI: https://ir.psgitech.ac.in/id/eprint/942

Actions (login required)

View Item
View Item