Related Experiment Video
Updated: Aug 12, 2025
04:48
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
2.9K
Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning
Jun Huang1, Tao Liu1, Beibei Qian1
1School of Computer and Information Engineering, Fuyang Normal University, Fuyang Anhui 236037, China.
Current Medical Imaging
|January 25, 2023
Summary
Deep learning segmentation accurately identifies lung tumors (LTs) and organs at risk (OARs) for radiation therapy. This review highlights advancements and challenges in deep learning for lung cancer radiotherapy (DSLC).
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Lung cancer exhibits the highest mortality rate globally.
- Radiation therapy (RT) is a primary treatment modality for lung cancer.
- Accurate segmentation of lung tumors (LTs) and organs at risk (OARs) is critical for effective RT.
Approach:
- A comprehensive literature search was conducted across four major databases (Web of Science, PubMed, Science Direct, Google Scholar) for studies published in the last decade.
- The review focused on advancements in deep learning-based segmentation technology for lung cancer radiotherapy (DSLC), analyzing methodologies for both LTs and OARs.
Key Points:
- Deep learning models demonstrate high performance in segmenting LTs, with Dice Similarity Coefficient (DSC) values generally exceeding 0.7.
- OAR segmentation using deep learning achieves even higher accuracy, with DSC indicators consistently above 0.8.
- The review synthesizes current DSLC research methods, identifies existing challenges, and proposes potential solutions.
Conclusions:
- Deep learning-based segmentation is a highly effective tool for improving the precision of lung cancer radiotherapy.
- Further research and collaboration are encouraged to advance the application of deep learning in DSLC.
- Addressing current challenges in DSLC can enhance treatment outcomes and patient care in lung cancer therapy.
