Research trends and hotspots in fundus image segmentation from 2007 to 2023: A bibliometric analysis
Compared with manual segmentation of fundus lesion images, the use of deep learning models for segmentation is more efficient and accurate, which is crucial for patients with eye diseases. Although the number of related publications globally is relatively small, a growing trend is still witnessed, with broad connections between countries and authors, mainly concentrated in East Asia and Europe. Research institutions in this field are limited, and hence, the research on diabetic retinopathy and retinal vessel segmentation should be strengthened to promote the development of this area.
Authors
- Shouying Chen
- Yiren Wang
- Yi Wang (ORCID: https://orcid.org/0000-0002-9013-7232)
- Zhongjian Wen
- Hairui Deng (ORCID: https://orcid.org/0009-0003-4391-657X)
- Ping Zhou (ORCID: https://orcid.org/0000-0002-2613-4563)
- Yongcheng He
- Shengmin Guo
- Victor Wen-Kai Cheng
Institutions
- Southwest Medical University (CN)
- Sichuan Agricultural University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2024-10-12
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e39329
- Citations
- 1
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.40