Literature DB >> 33284751

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

Yuhui Ma, Huaying Hao, Jianyang Xie, Huazhu Fu, Jiong Zhang, Jianlong Yang, Zhen Wang, Jiang Liu, Yalin Zheng, Yitian Zhao.   

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.

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Mesh:

Year:  2021        PMID: 33284751     DOI: 10.1109/TMI.2020.3042802

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  20 in total

Review 1.  In depth understanding of retinitis pigmentosa pathogenesis through optical coherence tomography angiography analysis: a narrative review.

Authors:  Bing-Wen Lu; Guo-Jun Chao; Gai-Ping Wu; Li-Ke Xie
Journal:  Int J Ophthalmol       Date:  2021-12-18       Impact factor: 1.779

2.  Dual-consistency semi-supervision combined with self-supervision for vessel segmentation in retinal OCTA images.

Authors:  Zailiang Chen; Yuchen Xiong; Hao Wei; Rongchang Zhao; Xuanchu Duan; Hailan Shen
Journal:  Biomed Opt Express       Date:  2022-04-21       Impact factor: 3.562

3.  Characterization of the Retinal Microvasculature and FAZ Changes in Ischemic Stroke and Its Different Types.

Authors:  Hongyu Duan; Jianyang Xie; Yifan Zhou; Hui Zhang; Yiyun Liu; Chuhao Tang; Yitian Zhao; Hong Qi
Journal:  Transl Vis Sci Technol       Date:  2022-10-03       Impact factor: 3.048

Review 4.  A Detailed Systematic Review on Retinal Image Segmentation Methods.

Authors:  Nihar Ranjan Panda; Ajit Kumar Sahoo
Journal:  J Digit Imaging       Date:  2022-05-04       Impact factor: 4.903

Review 5.  Artificial intelligence in OCT angiography.

Authors:  Tristan T Hormel; Thomas S Hwang; Steven T Bailey; David J Wilson; David Huang; Yali Jia
Journal:  Prog Retin Eye Res       Date:  2021-03-22       Impact factor: 21.198

Review 6.  Quantitative Assessment of Experimental Ocular Inflammatory Disease.

Authors:  Lydia J Bradley; Amy Ward; Madeleine C Y Hsue; Jian Liu; David A Copland; Andrew D Dick; Lindsay B Nicholson
Journal:  Front Immunol       Date:  2021-06-18       Impact factor: 7.561

7.  Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets.

Authors:  Dominik Drees; Aaron Scherzinger; René Hägerling; Friedemann Kiefer; Xiaoyi Jiang
Journal:  BMC Bioinformatics       Date:  2021-06-26       Impact factor: 3.169

Review 8.  Machine learning in optical coherence tomography angiography.

Authors:  David Le; Taeyoon Son; Xincheng Yao
Journal:  Exp Biol Med (Maywood)       Date:  2021-07-19

9.  Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning.

Authors:  Jinghui Lin; Lei Mou; Qifeng Yan; Shaodong Ma; Xingyu Yue; Shengjun Zhou; Zhiqing Lin; Jiong Zhang; Jiang Liu; Yitian Zhao
Journal:  Front Neurosci       Date:  2021-12-10       Impact factor: 4.677

10.  Keratoconus detection of changes using deep learning of colour-coded maps.

Authors:  Xu Chen; Jiaxin Zhao; Katja C Iselin; Davide Borroni; Davide Romano; Akilesh Gokul; Charles N J McGhee; Yitian Zhao; Mohammad-Reza Sedaghat; Hamed Momeni-Moghaddam; Mohammed Ziaei; Stephen Kaye; Vito Romano; Yalin Zheng
Journal:  BMJ Open Ophthalmol       Date:  2021-07-13
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