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Deep learning based brain tumor segmentation: a survey

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Deep learning based brain tumor segmentation: a survey. / Liu, Z.; Tong, L.; Chen, L. et al.
In: Complex and Intelligent Systems, Vol. 9, No. 1, 639, 28.02.2023, p. 1001-1026.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Liu, Z, Tong, L, Chen, L, Jiang, Z, Zhou, F, Zhang, Q, Zhang, X, Jin, Y & Zhou, H 2023, 'Deep learning based brain tumor segmentation: a survey', Complex and Intelligent Systems, vol. 9, no. 1, 639, pp. 1001-1026. https://doi.org/10.1007/s40747-022-00815-5

APA

Liu, Z., Tong, L., Chen, L., Jiang, Z., Zhou, F., Zhang, Q., Zhang, X., Jin, Y., & Zhou, H. (2023). Deep learning based brain tumor segmentation: a survey. Complex and Intelligent Systems, 9(1), 1001-1026. Article 639. https://doi.org/10.1007/s40747-022-00815-5

Vancouver

Liu Z, Tong L, Chen L, Jiang Z, Zhou F, Zhang Q et al. Deep learning based brain tumor segmentation: a survey. Complex and Intelligent Systems. 2023 Feb 28;9(1):1001-1026. 639. Epub 2022 Jul 9. doi: 10.1007/s40747-022-00815-5

Author

Liu, Z. ; Tong, L. ; Chen, L. et al. / Deep learning based brain tumor segmentation : a survey. In: Complex and Intelligent Systems. 2023 ; Vol. 9, No. 1. pp. 1001-1026.

Bibtex

@article{9011c480caa54839805b647879e24895,
title = "Deep learning based brain tumor segmentation: a survey",
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.",
keywords = "Brain tumor segmentation, Deep learning, Neural networks, Network design, Data imbalance, Multi-modalities",
author = "Z. Liu and L. Tong and L. Chen and Z. Jiang and F. Zhou and Q. Zhang and X. Zhang and Y. Jin and H. Zhou",
year = "2023",
month = feb,
day = "28",
doi = "10.1007/s40747-022-00815-5",
language = "English",
volume = "9",
pages = "1001--1026",
journal = "Complex and Intelligent Systems",
number = "1",

}

RIS

TY - JOUR

T1 - Deep learning based brain tumor segmentation

T2 - a survey

AU - Liu, Z.

AU - Tong, L.

AU - Chen, L.

AU - Jiang, Z.

AU - Zhou, F.

AU - Zhang, Q.

AU - Zhang, X.

AU - Jin, Y.

AU - Zhou, H.

PY - 2023/2/28

Y1 - 2023/2/28

N2 - 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.

AB - 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.

KW - Brain tumor segmentation

KW - Deep learning

KW - Neural networks

KW - Network design

KW - Data imbalance

KW - Multi-modalities

U2 - 10.1007/s40747-022-00815-5

DO - 10.1007/s40747-022-00815-5

M3 - Journal article

VL - 9

SP - 1001

EP - 1026

JO - Complex and Intelligent Systems

JF - Complex and Intelligent Systems

IS - 1

M1 - 639

ER -