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Research progress of multimodal MRI brain tumor image segmentation methods
SUN Kangkang  CHEN Wei  LI Qixuan  SUN Jiawei  JIAO Zhuqing  NI Xinye 

Cite this article as: SUN K K, CHEN W, LI Q X, et al. Research progress of multimodal MRI brain tumor image segmentation methods[J]. Chin J Magn Reson Imaging, 2023, 14(11): 164-169, 176. DOI:10.12015/issn.1674-8034.2023.11.028.


[Abstract] MRI is a non-invasive multimodal imaging method, which is widely used in the detection and diagnosis of brain tumors. Multimodal MRI brain tumor image segmentation has important significance for the diagnosis and treatment of brain tumors. At present, most of the segmentation work is still manually completed by doctors, with low efficiency and strong subjectivity. Therefore, seeking an efficient and accurate automatic segmentation method for brain tumors is crucial for clinical applications. We reviewed the research progress of brain tumor segmentation based on multimodal MRI images, compared and analyzed traditional segmentation methods and deep learning based segmentation methods in this paper, and then summarized the problems of existing brain tumor image segmentation methods and makes prospects, so that researchers in this field could better understand the current research progress of multimodal MRI brain tumor image segmentation methods.
[Keywords] magnetic resonance imaging；brain tumor；multimodal；image segmentation；deep learning

SUN Kangkang1, 2, 3   CHEN Wei1, 2, 3   LI Qixuan2, 3   SUN Jiawei2, 3   JIAO Zhuqing1   NI Xinye2, 3*  

1 School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China

2 Department of Radiotherapy, the Second People's Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China

3 Central Laboratory of Medical Physics, Nanjing Medical University, Changzhou 213003, China

Corresponding author: NI X Y, E-mail: nxy@njmu.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS Jiangsu Provincial Key Research and Development Program Social Development Project (No. BE2022720); General Program of Jiangsu Provincial Health Commission (No. M2020006).
Received  2023-04-26
Accepted  2023-10-23
DOI: 10.12015/issn.1674-8034.2023.11.028
Cite this article as: SUN K K, CHEN W, LI Q X, et al. Research progress of multimodal MRI brain tumor image segmentation methods[J]. Chin J Magn Reson Imaging, 2023, 14(11): 164-169, 176. DOI:10.12015/issn.1674-8034.2023.11.028.

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