Work

Research trends and hotspots in fundus image segmentation from 2007 to 2023: A bibliometric analysis

Year: 2024

Type: article

Abstract: Objective: To understand the current status, research hotspots, and trends of automatic segmentation of fundus lesion images worldwide, providing a reference for subsequent related studies. Methods: The electronic database Web of Science Core Collection was searched for research in the field of automatic segmentation of fundus lesion images from 2007 to 2023. Visualization maps of countries, authors, institutions, journals, references, and keywords were generated and analyzed using the CiteSpace and VOSviewer software. Results: . Conclusions: 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.

Source: Heliyon

DOI: 10.1016/j.heliyon.2024.e39329