Deep learning for automatic target volume segmentation in radiation therapy: A review

Hui Lin, Haonan Xiao, Lei Dong, Kevin Boon Keng Teo, Wei Zou, Jing Cai, Taoran Li

Research output: Journal article publicationReview articleAcademic researchpeer-review

12 Citations (Scopus)


Deep learning, a new branch of machine learning algorithm, has emerged as a fast growing trend in medical imaging and become the state-of-the-art method in various clinical applications such as Radiology, Histo-pathology and Radiation Oncology. Specifically in radiation oncology, deep learning has shown its power in performing automatic segmentation tasks in radiation therapy for Organs-At-Risks (OAR), given its potential in improving the efficiency of OAR contouring and reducing the inter- and intra-observer variabilities. The similar interests were shared for target volume segmentation, an essential step of radiation therapy treatment planning, where the gross tumor volume is defined and microscopic spread is encompassed. The deep learning-based automatic segmentation method has recently been expanded into target volume automatic segmentation. In this paper, the authors summarized the major deep learning architectures of supervised learning fashion related to target volume segmentation, reviewed the mechanism of each infrastructure, surveyed the use of these models in various imaging domains (including Computational Tomography with and without contrast, Magnetic Resonant Imaging and Positron Emission Tomography) and multiple clinical sites, and compared the performance of different models using standard geometric evaluation metrics. The paper concluded with a discussion of open challenges and potential paths of future research in target volume automatic segmentation and how it may benefit the clinical practice.

Original languageEnglish
Pages (from-to)4847-4858
Number of pages12
JournalQuantitative Imaging in Medicine and Surgery
Issue number12
Publication statusPublished - Dec 2021


  • Auto segmentation
  • Deep learning
  • Radiation therapy
  • Target volume delineation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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