TY - JOUR
T1 - Deep Iterative Vessel Segmentation in OCT Angiography
AU - Pissas, Theodoros
AU - Bloch, Edward
AU - Cardoso, M. Jorge
AU - Flores, Blanca
AU - Georgiadis, Odysseas
AU - Jalali, Sepehr
AU - Ravasio, Claudio
AU - Stoyanov, Danail
AU - Da Cruz, Lyndon
AU - Bergeles, Christos
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85084680470&partnerID=8YFLogxK
U2 - 10.1364/BOE.384919
DO - 10.1364/BOE.384919
M3 - Article
AN - SCOPUS:85084680470
SN - 2156-7085
VL - 11
SP - 2490
EP - 2510
JO - Biomedical Optics Express
JF - Biomedical Optics Express
IS - 5
ER -