Deep learning techniques for tumor segmentation: a review

Huiyan Jiang, Zhaoshuo Diao, Yu Dong Yao

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

Recently, deep learning, especially convolutional neural networks, has achieved the remarkable results in natural image classification and segmentation. At the same time, in the field of medical image segmentation, researchers use deep learning techniques for tasks such as tumor segmentation, cell segmentation, and organ segmentation. Automatic tumor segmentation plays an important role in radiotherapy and clinical practice and is the basis for the implementation of follow-up treatment programs. This paper reviews the tumor segmentation methods based on deep learning in recent years. We first introduce the common medical image types and the evaluation criteria of segmentation results in tumor segmentation. Then, we review the tumor segmentation methods based on deep learning from technique view and tumor view, respectively. The technique view reviews the researches from the architecture of the deep learning and the tumor view reviews from the type of tumors.

Original languageEnglish
Pages (from-to)1807-1851
Number of pages45
JournalJournal of Supercomputing
Volume78
Issue number2
DOIs
StatePublished - Feb 2022

Keywords

  • Deep learning
  • Medical image
  • Review
  • Tumor segmentation

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