Open AccessDeep iterative vessel segmentation in OCT angiography
Author(s) -
Theodoros Pissas,
Edward Bloch,
M. Jorge Cardoso,
Blanca Flores,
Odysseas Georgiadis,
Sepehr Jalali,
Claudio S. Ravasio,
Danail Stoyanov,
Lyndon da Cruz,
Christos Bergeles
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.384919
Subject(s) - optical coherence tomography , segmentation , computer science , artificial intelligence , convolutional neural network , surgical planning , computer vision , image segmentation , retinal , medicine , radiology , ophthalmology
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 Plexus in 2D 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 from 50 subjects, including both individuals that underwent surgery for structural macular abnormalities and healthy subjects. Additionally, we demonstrate generalization to 3D segmentation 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.