ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

Ma, Yuhui, Hao, Huaying, Xie, Jianyang ORCID: 0000-0002-4565-5807, Fu, Huazhu, Zhang, Jiong, Yang, Jianlong, Wang, Zhen, Liu, Jiang, Zheng, Yalin ORCID: 0000-0002-7873-0922 and Zhao, Yitian (2021) ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model. IEEE TRANSACTIONS ON MEDICAL IMAGING, 40 (3). pp. 928-939.

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Official URL: http://dx.doi.org/10.1109/tmi.2020.3042802

Abstract

Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that has been increasingly used to image the retinal vasculature at capillary level resolution. However, automated segmentation of retinal vessels in OCTA has been under-studied due to various challenges such as low capillary visibility and high vessel complexity, despite its significance in understanding many vision-related diseases. In addition, there is no publicly available OCTA dataset with manually graded vessels for training and validation of segmentation algorithms. To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCTA SEgmentation dataset (ROSE), which consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level. This dataset with the source code has been released for public access to assist researchers in the community in undertaking research in related topics. Secondly, we introduce a novel split-based coarse-to-fine vessel segmentation network for OCTA images (OCTA-Net), with the ability to detect thick and thin vessels separately. In the OCTA-Net, a split-based coarse segmentation module is first utilized to produce a preliminary confidence map of vessels, and a split-based refined segmentation module is then used to optimize the shape/contour of the retinal microvasculature. We perform a thorough evaluation of the state-of-the-art vessel segmentation models and our OCTA-Net on the constructed ROSE dataset. The experimental results demonstrate that our OCTA-Net yields better vessel segmentation performance in OCTA than both traditional and other deep learning methods. In addition, we provide a fractal dimension analysis on the segmented microvasculature, and the statistical analysis demonstrates significant differences between the healthy control and Alzheimer's Disease group. This consolidates that the analysis of retinal microvasculature may offer a new scheme to study various neurodegenerative diseases.

Item Type:	Article
Uncontrolled Keywords:	Measurement, Image segmentation, Statistical analysis, Optical coherence tomography, Ultraviolet sources, Retina, Diseases, Optical coherence tomography angiography, vessel segmentation, deep network, benchmark
Depositing User:	Symplectic Admin
Date Deposited:	08 Jan 2021 14:20
Last Modified:	18 Jan 2023 23:04
DOI:	10.1109/TMI.2020.3042802
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3112860