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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Tumor Image Segmentation: A Bibliometric Analysis from 2003 to 2024.

Zhenghao Chen1, Zhongqing Wang2, He Ma1

  • 1College of Medicine and Biological Information Engineering, Northeastern University, 195 Chuangxin Road, Hunnan District, Shenyang, China.

Current Pharmaceutical Biotechnology
|March 20, 2025
PubMed
Summary
This summary is machine-generated.

Bibliometrics reveals a surge in tumor image segmentation research, with China leading publications. Advances in methods like U-Net and MAMBA are improving disease prevention and monitoring.

Keywords:
MAMBA.Tumor imageU-netVOSviewerbibliometricsimage segmentation

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Area of Science:

  • Medical Imaging Analysis
  • Bibliometrics
  • Artificial Intelligence in Oncology

Background:

  • Bibliometric analysis is a valuable tool for understanding research trends and hotspots in scientific fields.
  • Tumor image segmentation is a critical area of study with significant implications for disease diagnosis and treatment.

Purpose of the Study:

  • To conduct a bibliometric analysis of tumor image segmentation research.
  • To identify current research hotspots, trends, and emerging topics in tumor image segmentation.
  • To provide research guidelines for scholars in the field.

Main Methods:

  • Bibliometric analysis of 3377 articles published between 2003 and 2024, sourced from the Web of Science database.
  • Analysis of publication volume, country/region, institution, journal, author, keywords, and references.
  • Visualization of co-authorship, co-citation, and co-occurrence using VOSviewer.

Main Results:

  • A significant increase in publications since 2016, with 576 articles in 2023.
  • Mainland China leads in publication volume, while IEEE Transactions on Medical Imaging is the most cited journal.
  • Key research clusters identified: segmentation methods, applications, CT-based segmentation, and MRI-based segmentation, with Transformer, Attention Mechanism, and U-Net as emerging keywords.

Conclusions:

  • Tumor image segmentation research has grown steadily, with rapid advancements in methods like U-Net and MAMBA.
  • These advancements are crucial for improving disease prevention and monitoring.
  • The study highlights global collaboration and provides insights into research trends and future directions.