Research on Segmentation Technology in Lung Cancer Radiotherapy
Based on Deep Learning

Jun      Huang; Tao      Liu; Beibei      Qian; Zhibo      Chen; Ya      Wang
Abstract

Background: Lung cancer has the highest mortality rate among cancers. Radiation therapy (RT) is one of the most effective therapies for lung cancer. The correct segmentation of lung tumors (LTs) and organs at risk (OARs) is the cornerstone of successful RT.
Methods: We searched four databases for relevant material published in the last 10 years: Web of Science, PubMed, Science Direct, and Google Scholar. The advancement of deep learning-based segmentation technology for lung cancer radiotherapy (DSLC) research was examined from the perspectives of LTs and OARs.
Results: In this paper, Most of the dice similarity coefficient (DSC) values of LT segmentation in the surveyed literature were above 0.7, whereas the DSC indicators of OAR segmentation were all over 0.8.
Conclusion: The contribution of this review is to summarize DSLC research methods and the issues that DSLC faces are discussed, as well as possible viable solutions. The purpose of this review is to encourage collaboration among experts in lung cancer radiotherapy and DL and to promote more research into the use of DL in lung cancer radiotherapy.
Keywords: Lung cancer, deep learning, image segmentation, organs at risk, lung tumors, radiation therapy.
Graphical Abstract

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Cite As
Current Medical Imaging

Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning

Abstract

Graphical Abstract
