A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis

Zhang, Bin and Rahmatullah, Bahbibi and Li Wang, Shir and Zhang, Guangnan and Wang, Huan and Ebrahim, Nader Ale (2021) A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis. Journal of Applied Clinical Medical Physics, 22 (10). pp. 45-65. ISSN 1526-9914, DOI https://doi.org/10.1002/acm2.13394.

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Purpose Medical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation. Methods This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates. Results The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers. Conclusions Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.

Item Type: Article
Uncontrolled Keywords: Bibliometric; Image segmentation; Medical image; Publication trends; Research productivity
Subjects: R Medicine
R Medicine > R Medicine (General) > Medical technology
Divisions: Deputy Vice Chancellor (Research & Innovation) Office
Depositing User: Ms Zaharah Ramly
Date Deposited: 26 Jul 2022 08:25
Last Modified: 26 Jul 2022 08:25
URI: http://eprints.um.edu.my/id/eprint/28192

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