Machine learning and deep learning for brain tumor MRI image segmentation

被引:12
作者
Khan, Md Kamrul Hasan [1 ]
Guo, Wenjing [1 ]
Liu, Jie [1 ]
Dong, Fan [1 ]
Li, Zoe [1 ]
Patterson, Tucker A. [1 ]
Hong, Huixiao [1 ]
机构
[1] US FDA, Natl Ctr Toxicol Res, Jefferson, AR 72079 USA
关键词
Machine learning; deep learning; brain; tumor; image segmentation; magnetic resonance imaging; GAUSSIAN MIXTURE MODEL; NEURAL-NETWORKS;
D O I
10.1177/15353702231214259
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.
引用
收藏
页码:1974 / 1992
页数:19
相关论文
共 131 条
[1]   Brain tumor segmentation based on a hybrid clustering technique [J].
Abdel-Maksoud, Eman ;
Elmogy, Mohammed ;
Al-Awadi, Rashid .
EGYPTIAN INFORMATICS JOURNAL, 2015, 16 (01) :71-81
[2]   HTTU-Net: Hybrid Two Track U-Net for Automatic Brain Tumor Segmentation [J].
Aboelenein, Nagwa M. ;
Piao Songhao ;
Koubaa, Anis ;
Noor, Alam ;
Afifi, Ahmed .
IEEE ACCESS, 2020, 8 :101406-101415
[3]   Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing [J].
AlBadawy, Ehab A. ;
Saha, Ashirbani ;
Mazurowski, Maciej A. .
MEDICAL PHYSICS, 2018, 45 (03) :1150-1158
[4]   Extending 2D Deep Learning Architectures to 3D Image Segmentation Problems [J].
Albiol, Alberto ;
Albiol, Antonio ;
Albiol, Francisco .
BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES, BRAINLES 2018, PT II, 2019, 11384 :73-82
[5]   Combined Features in Region of Interest for Brain Tumor Segmentation [J].
Alqazzaz, Salma ;
Sun, Xianfang ;
Nokes, Len Dm ;
Yang, Hong ;
Yang, Yingxia ;
Xu, Ronghua ;
Zhang, Yanqiang ;
Yang, Xin .
JOURNAL OF DIGITAL IMAGING, 2022, 35 (04) :938-946
[6]  
[Anonymous], 2014, P 2014 36 ANN INT C
[7]  
Ayachi R, 2009, LECT NOTES COMPUT SC, V5590, P736, DOI 10.1007/978-3-642-02906-6_63
[8]  
Babu KR., 2021, CURR MED IMAGING, V17
[9]   SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [J].
Badrinarayanan, Vijay ;
Kendall, Alex ;
Cipolla, Roberto .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (12) :2481-2495
[10]  
Bahadure NB, 2017, INT J BIOMED IMAGING, V2017, DOI 10.1155/2017/9749108