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Deep learning based brain tumor segmentation: a survey

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print
  • Z. Liu
  • L. Tong
  • L. Chen
  • Z. Jiang
  • F. Zhou
  • Q. Zhang
  • X. Zhang
  • Y. Jin
  • H. Zhou
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Article number639
<mark>Journal publication date</mark>9/07/2022
<mark>Journal</mark>Complex and Intelligent Systems
Number of pages26
Publication StatusE-pub ahead of print
Early online date9/07/22
<mark>Original language</mark>English

Abstract

Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we provide this survey with a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 150 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions.