TY - GEN
T1 - Classification of Retinal Vessels into Artery-Vein in OCT Angiography Guided by Fundus Images
AU - Xie, Jianyang
AU - Liu, Yonghuai
AU - Zheng, Yalin
AU - Su, Pan
AU - Hu, Yan
AU - Yang, Jianlong
AU - Liu, Jiang
AU - Zhao, Yitian
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Automated classification of retinal artery (A) and vein (V) is of great importance for the management of eye diseases and systemic diseases. Traditional colour fundus images usually provide a large field of view of the retina in color, but often fail to capture the finer vessels and capillaries. In contrast, the new Optical Coherence Tomography Angiography (OCT-A) images can provide clear view of the retinal microvascular structure in gray scale down to capillary levels but cannot provide A/V information alone. For the first time, this study presents a new approach for the classification of A/V in OCT-A images, guided by the corresponding fundus images, so that the strengths of both modalities can be integrated together. To this end, we first estimate the vascular topologies of paired color fundus and OCT-A images respectively, then we propose a topological message passing algorithm to register the OCT-A onto color fundus images, and finally the integrated vascular topology map is categorized into arteries and veins by a clustering approach. The proposed method has been applied to a local dataset contains both fundus image and OCT-A, and it reliably identified individual arteries and veins in OCT-A. The experimental results show that despite lack of color and intensity information, it produces promising results. In addition, we will release our database to the public.
AB - Automated classification of retinal artery (A) and vein (V) is of great importance for the management of eye diseases and systemic diseases. Traditional colour fundus images usually provide a large field of view of the retina in color, but often fail to capture the finer vessels and capillaries. In contrast, the new Optical Coherence Tomography Angiography (OCT-A) images can provide clear view of the retinal microvascular structure in gray scale down to capillary levels but cannot provide A/V information alone. For the first time, this study presents a new approach for the classification of A/V in OCT-A images, guided by the corresponding fundus images, so that the strengths of both modalities can be integrated together. To this end, we first estimate the vascular topologies of paired color fundus and OCT-A images respectively, then we propose a topological message passing algorithm to register the OCT-A onto color fundus images, and finally the integrated vascular topology map is categorized into arteries and veins by a clustering approach. The proposed method has been applied to a local dataset contains both fundus image and OCT-A, and it reliably identified individual arteries and veins in OCT-A. The experimental results show that despite lack of color and intensity information, it produces promising results. In addition, we will release our database to the public.
KW - Artery/vein classification
KW - Message passing
KW - OCT-A
UR - http://www.scopus.com/inward/record.url?scp=85092794064&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59725-2_12
DO - 10.1007/978-3-030-59725-2_12
M3 - Conference contribution
AN - SCOPUS:85092794064
SN - 9783030597245
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 117
EP - 127
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
A2 - Martel, Anne L.
A2 - Abolmaesumi, Purang
A2 - Stoyanov, Danail
A2 - Mateus, Diana
A2 - Zuluaga, Maria A.
A2 - Zhou, S. Kevin
A2 - Racoceanu, Daniel
A2 - Joskowicz, Leo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 8 October 2020
ER -