Deep learning based brain tumor segmentation: a survey

• Published in: Complex & intelligent systems Vol. 9; no. 1; pp. 1001 - 1026
• Main Authors: Liu, Zhihua, Tong, Lei, Chen, Long, Jiang, Zheheng, Zhou, Feixiang, Zhang, Qianni, Zhang, Xiangrong, Jin, Yaochu, Zhou, Huiyu
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2023; Springer Nature B.V; Springer
• Subjects: Artificial intelligence; Brain; Brain cancer; Brain research; Brain tumor segmentation; Complexity; Computational Intelligence; Computer architecture; Computer vision; Data imbalance; Data Structures and Information Theory; Deep learning; Engineering; Image analysis; Image classification; Image segmentation; Intelligent systems; Machine learning; Magnetic resonance imaging; Medical imaging; Multi-modalities; Network design; Neural networks; Neuroimaging; Object recognition; Survey and State of the Art; Tumors; Deep learning; Brain tumor segmentation; Network design; Neural networks; Multi-modalities; Data imbalance
• ISSN: 2199-4536; 2198-6053
• DOI: 10.1007/s40747-022-00815-5

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.