Medical Image Segmentation Analysis and Research

Xiaohan Liu

2025

Abstract

Medical image segmentation technology, a vital part of medical imaging analysis, has made great progress in recent years. It is extremely important for the early identification of diseases, the making of treatment plans, and surgical planning. In the early days, traditional image segmentation methods, like those based on threshold-based segmentation, edge-detection, and region-growing, were effective in some simple scenarios. However, when they were faced with complex medical images, they often encountered challenges such as difficulty in handling noise interference, blurred boundaries, and multi-target overlapping. This paper first systematically reviews three traditional medical image segmentation techniques based on threshold, edge, and region, and then focuses on recent deep-learning-based segmentation techniques, including U-Net, Mask R-CNN, and DeepLab models. This paper also summarizes the current status of medical image segmentation techniques through examples of cell and organ segmentation as well as stomach cancer segmentation. Finally, from the aspects of deep learning model optimization and technology integration, this paper looks into the future of medical image segmentation technology.

Paper Citation

in Harvard Style

Liu X. (2025). Medical Image Segmentation Analysis and Research. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 194-198. DOI: 10.5220/0013680900004670

in Bibtex Style

@conference{icdse25,
author={Xiaohan Liu},
title={Medical Image Segmentation Analysis and Research},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={194-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013680900004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Medical Image Segmentation Analysis and Research
SN - 978-989-758-765-8
AU - Liu X.
PY - 2025
SP - 194
EP - 198
DO - 10.5220/0013680900004670
PB - SciTePress