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Deep learning based brain tumor segmentation: a survey

Liu, Zhihua; Tong, Lei; Chen, Long; Jiang, Zheheng; Zhou, Feixiang; Zhang, Qianni; Zhang, Xiangrong; ... Zhou, Huiyu; + view all (2022) Deep learning based brain tumor segmentation: a survey. Complex & Intelligent Systems 10.1007/s40747-022-00815-5. (In press). Green open access

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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.

Type: Article
Title: Deep learning based brain tumor segmentation: a survey
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s40747-022-00815-5
Publisher version: https://doi.org/10.1007/s40747-022-00815-5
Language: English
Additional information: © 2023 Springer Nature Switzerland AG. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: Brain tumor segmentation, Deep learning, Neural networks, Network design, Data imbalance, Multi-modalitie
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10163777
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