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).
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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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