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Deep Iterative Vessel Segmentation in OCT Angiography

Research output: Contribution to journalArticle

Theodoros Pissas, Edward Bloch, M. Jorge Cardoso, Blanca Flores, Odysseas Georgiadis, Sepehr Jalali, Claudio Ravasio, Danail Stoyanov, Lyndon Da Cruz, Christos Bergeles

Original languageEnglish
Pages (from-to)2490-2510
Number of pages21
JournalBiomedical Optics Express
Volume11
Issue number5
DOIs
Publication statusPublished - 1 May 2020

King's Authors

Abstract

This paper addresses retinal vessel segmentation on Optical Coherence Tomography Angiography (OCT-A) images of the human retina. Our approach is motivated by the need for high precision image-guided delivery of regenerative therapies in vitreo-retinal surgery. OCT-A visualizes macular vasculature, the main landmark of the surgically targeted area, at a level of detail and spatial extent unattainable by other imaging modalities. Thus, automatic extraction of detailed vessel maps can ultimately inform surgical planning. We address the task of delineation of the Superficial Vascular Plexusin2D Maximum Intensity Projections (MIP) of OCT-A using convolutional neural networks that iteratively refine the quality of the produced vessel segmentations. We demonstrate that the proposed approach compares favourably to alternative network baselines and graph-based methodologies through extensive experimental analysis, using data collected from50subjects, including both individuals that underwent surgery for structural macular abnormalities and healthy subjects. Additionally, we demonstrate generalization to3Dsegmentation and narrower field-of-view OCT-A. In the future, the extracted vessel maps will be leveraged for surgical planning and semi-automated intraoperative navigation in vitreo-retinal surgery.

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