Soul: An OCTA dataset based on Human Machine Collaborative Annotation Framework

• Published in: Scientific data Vol. 11; no. 1; pp. 838 - 9
• Main Authors: Xue, Jingyan, Feng, Zhenhua, Zeng, Lili, Wang, Shuna, Zhou, Xuezhong, Xia, Jianan, Deng, Aijun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.08.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1314; 631/114/2397; 631/114/2402; Angiography; Blood vessels; Collaboration; Data Descriptor; Datasets; Datasets as Topic; Eye diseases; Humanities and Social Sciences; Humans; Information processing; Machine Learning; multidisciplinary; Retina; Retinal Vein Occlusion; Retinal Vessels - diagnostic imaging; Science; Science (multidisciplinary); Tomography, Optical Coherence; Vascular diseases; Visual perception
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-024-03665-7

Branch retinal vein occlusion (BRVO) is the most prevalent retinal vascular disease that constitutes a threat to vision due to increased venous pressure caused by venous effluent in the space, leading to impaired visual function. Optical Coherence Tomography Angiography (OCTA) is an innovative non-invasive technique that offers high-resolution three-dimensional structures of retinal blood vessels. Most publicly available datasets are collected from single visits with different patients, encompassing various eye diseases for distinct tasks and areas. Moreover, due to the intricate nature of eye structure, professional labeling not only relies on the expertise of doctors but also demands considerable time and effort. Therefore, we have developed a BRVO-focused dataset named Soul (Source of ocular vascular) and propose a human machine collaborative annotation framework (HMCAF) using scrambled retinal blood vessels data. Soul is categorized into 6 subsets based on injection frequency and follow-up duration. The dataset comprises original images, corresponding blood vessel labels, and clinical text information sheets which can be effectively utilized when combined with machine learning.