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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.

[1]
GUPTA V, BIBHU V. Deep residual network based brain tumor segmentation and detection with MRI using improved invasive bat algorithm[J]. Multimed Tools Appl, 2023, 82(8): 12445-12467. DOI: 10.1007/s11042-022-13769-0.
[2]
ADEL FAHMIDEH M, SCHRAW J M, CHINTAGUMPALA M, et al. Neighborhood socioeconomic deprivation and mortality in children with central nervous system tumors[J]. Cancer Epidemiol Biomarkers Prev, 2021, 30(12): 2278-2285. DOI: 10.1158/1055-9965.EPI-21-0368.
[3]
LIU L, KUANG L, JI Y F. Multimodal MRI brain tumor image segmentation using sparse subspace clustering algorithm[J/OL]. Comput Math Methods Med, 2020, 2020: 8620403 [2023-04-25]. https://pubmed.ncbi.nlm.nih.gov/32714431/. DOI: 10.1155/2020/8620403.
[4]
ZHU Z Q, HE X Y, QI G Q, et al. Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI[J/OL]. Inf Fusion, 2023, 91: 376-387 [2023-04-25]. https://www.sciencedirect.com/science/article/abs/pii/S1566253522001981?via%3Dihub. DOI: 10.1016/j.inffus.2022.10.022.
[5]
ZHANG H, ZHANG S, SUN J W, et al. Research progress of deep learning brain tumor MRI image classification[J]. Chin J Magn Reson Imag, 2023, 14(1): 166-171, 193. DOI: 10.12015/issn.1674-8034.2023.01.031.
[6]
SCOLA E, DEL VECCHIO G, BUSTO G, et al. Conventional and advanced magnetic resonance imaging assessment of non-enhancing peritumoral area in brain tumor[J/OL]. Cancers, 2023, 15(11): 2992 [2023-04-25]. https://pubmed.ncbi.nlm.nih.gov/37296953/. DOI: 10.3390/cancers15112992.
[7]
ZHOU R W, HU S J, MA B X, et al. Automatic segmentation of MRI of brain tumor using deep convolutional network[J/OL]. Biomed Res Int, 2022, 2022: 4247631 [2023-04-25]. https://pubmed.ncbi.nlm.nih.gov/35757482/. DOI: 10.1155/2022/4247631.
[8]
LIU Y, WANG X P, WANG J Q, et al. Watershed algorithm for brain tumor segmentation based on morphological reconstruction and gradient layered modification[J]. Appl Res Comput, 2015, 32(8): 2487-2491. DOI: 10.3969/j.issn.1001-3695.2015.08.060.
[9]
RAJAMANI K ,BENSON C C, DEEPA V, et al. Brain tumor segmentation from MR brain images using improved fuzzy c-means clustering and watershed algorithm[C]//2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). September 21-24, 2016, Jaipur, India. IEEE, 2016: 187-192. DOI: 10.1109/ICACCI.2016.7732045.
[10]
SIVAKUMAR V, JANAKIRAMAN N. A novel method for segmenting brain tumor using modified watershed algorithm in MRI image with FPGA[J/OL]. Biosystems, 2020, 198: 104226 [2023-04-25]. https://pubmed.ncbi.nlm.nih.gov/32861800/. DOI: 10.1016/j.biosystems.2020.104226.
[11]
HATCHOLLI SEERE S K, KARIBASAPPA K. Threshold segmentation and watershed segmentation algorithm for brain tumor detection using support vector machine[J]. Eur J Eng Technol Res, 2020, 5(4): 516-519. DOI: 10.24018/ejeng.2020.5.4.1902.
[12]
LI W, CHEN W F. Graph-based interactive three-dimensional segmentation of magnetic resonance images of brain tumors[J]. J South Med Univ, 2009, 29(1): 140-143. DOI: 10.3321/j.issn:1673-4254.2009.01.024.
[13]
GAMMOUDI I, MAHJOUB M ALI. Brain tumor segmentation using community detection algorithm[C]//2021 International Conference on Cyberworlds (CW). September 28-30, 2021, Caen, France. IEEE, 2021: 57-63. DOI: 10.1109/CW52790.2021.00016.
[14]
MAMATHA S K, KRISHNAPPA H K, SHALINI N. Graph theory based segmentation of magnetic resonance images for brain tumor detection[J]. Pattern Recognit Image Anal, 2022, 32(1): 153-161. DOI: 10.1134/S1054661821040167.
[15]
JANIESCH C, ZSCHECH P, HEINRICH K. Machine learning and deep learning[J]. Electron Markets, 2021, 31(3): 685-695. DOI: 10.1007/s12525-021-00475-2.
[16]
KONG L J, WANG Q W, BAO Y C, et al. A survey on medical image segmentation based on deep learning[J]. Radio Commun Technol, 2021, 47(2): 121-130. DOI: 10.3969/j.issn.1003-3114.2021.02.001.
[17]
MAGADZA T, VIRIRI S. Deep learning for brain tumor segmentation: a survey of state-of-the-art[J/OL]. J Imaging, 2021, 7(2): 19 [2023-04-25]. https://pubmed.ncbi.nlm.nih.gov/34460618/. DOI: 10.3390/jimaging7020019.
[18]
LIU Z H, TONG L, CHEN L, et al. Deep learning based brain tumor segmentation: a survey[J]. Complex Intell Syst, 2023, 9(1): 1001-1026. DOI: 10.1007/s40747-022-00815-5.
[19]
JIANG Z K, LYU X G, ZHANG J X, et al. Review of deep learning methods for MRI brain tumor image segmentation[J]. J Image Graph, 2020, 25(2): 215-228.
[20]
SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651. DOI: 10.1109/TPAMI.2016.2572683.
[21]
RONNEBERGER O. Invited talk: U-net convolutional networks for biomedical image segmentation[C]//Bildverarbeitung für die Medizin 2017. Berlin, Heidelberg: Springer Vieweg, 2017: 3-3. DOI: 10.1007/978-3-662-54345-0_3.
[22]
OKTAY O, SCHLEMPER J, LE FOLGOC L, et al. Attention U-net: learning where to look for the pancreas[EB/OL]. 2018: arXiv: 1804.03999. https://arxiv.org/abs/1804.03999.pdf
[23]
ÇIÇEK Ö, ABDULKADIR A, LIENKAMP S S, et al. 3D U-net: learning dense volumetric segmentation from sparse annotation[C]//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part Ⅱ. New York: ACM, 2016: 424-432. DOI: 10.1007/978-3-319-46723-8_49.
[24]
MILLETARI F, NAVAB N, AHMADI S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C]//2016 Fourth International Conference on 3D Vision (3DV). October 25-28, 2016, Stanford, CA, USA. IEEE, 2016: 565-571. DOI: 10.1109/3DV.2016.79.
[25]
KERMI A, MAHMOUDI I, KHADIR M T. Deep convolutional neural networks using U-net for automatic brain tumor segmentation in multimodal MRI volumes[C]//CRIMI A, BAKAS S, KUIJF H, et al. International MICCAI Brainlesion Workshop. Cham: Springer, 2019: 37-48. DOI: 10.1007/978-3-030-11726-9_4.
[26]
NOORI M, BAHRI A, MOHAMMADI K. Attention-guided version of 2D UNet for automatic brain tumor segmentation[C]//2019 9th International Conference on Computer and Knowledge Engineering (ICCKE). October 24-25, 2019, Mashhad, Iran. IEEE, 2020: 269-275. DOI: 10.1109/ICCKE48569.2019.8964956.
[27]
XUE Y Z, XIE M Y, FARHAT F G, et al. A multi-path decoder network for brain tumor segmentation[M]//Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer International Publishing, 2020: 255-265. DOI: 10.1007/978-3-030-46643-5_25.
[28]
PENG S T, CHEN W, SUN J W, et al. Multi-Scale 3D U-Nets: an approach to automatic segmentation of brain tumor[J]. Int J Imaging Syst Tech, 2020, 30(1): 5-17. DOI: 10.1002/ima.22368.
[29]
ZHOU Z X, HE Z S, JIA Y Y. AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images[J/OL]. Neurocomputing, 2020, 402: 235-244 [2023-04-25]. https://www.sciencedirect.com/science/article/abs/pii/S0925231220304847?via%3Dihub. DOI: 10.1016/j.neucom.2020.|03.097.
[30]
ZHOU X Y, LI X Y, HU K, et al. ERV-Net: an efficient 3D residual neural network for brain tumor segmentation[J/OL]. Expert Syst Appl, 2021, 170: 114566 [2023-04-25]. https://www.sciencedirect.com/science/article/abs/pii/S0957417421000075?via%3Dihub. DOI: 10.1016/j.eswa.2021.114566.
[31]
ZHANG D W, HUANG G H, ZHANG Q A, et al. Cross-modality deep feature learning for brain tumor segmentation[J/OL]. Pattern Recognit, 2021, 110: 107562 [2023-04-25]. https://www.sciencedirect.com/science/article/abs/pii/S0031320320303654?via%3Dihub. DOI: 10.1016/j.patcog.2020.107562.
[32]
ZHOU T X, CANU S, VERA P, et al. Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing MR modalities[J/OL]. Neurocomputing, 2021, 466: 102-112 [2023-04-25]. https://www.sciencedirect.com/science/article/abs/pii/S0925231221013904?via%3Dihub. DOI: 10.1016/j.neucom.2021.09.032.
[33]
TIAN W W, LI D W, LV M Y, et al. Axial attention convolutional neural network for brain tumor segmentation with multi-modality MRI scans[J/OL]. Brain Sci, 2022, 13(1): 12 [2023-04-25]. https://pubmed.ncbi.nlm.nih.gov/36671994/. DOI: 10.3390/brainsci13010012.
[34]
REZAEI M, YANG H J, MEINEL C. Voxel-GAN: adversarial framework for learning imbalanced brain tumor segmentation[M]//Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer International Publishing, 2019: 321-333. DOI: 10.1007/978-3-030-11726-9_29.
[35]
CIRILLO M D, ABRAMIAN D, EKLUND A. Vox2Vox: 3D-GAN for brain tumour segmentation[M]//Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer International Publishing, 2021: 274-284. DOI: 10.1007/978-3-030-72084-1_25.
[36]
DING Y, ZHANG C, CAO M S, et al. ToStaGAN: an end-to-end two-stage generative adversarial network for brain tumor segmentation[J]. Neurocomputing, 2021, 462: 141-153. DOI: 10.1016/j.neucom.2021.07.066.
[37]
KHANH PHUNG L, VAN NGUYEN S, DUY LE T, et al. A research for segmentation of brain tumors based on GAN model[C]//NGUYEN NT, TRAN TK, TUKAYEV U, et al. Asian Conference on Intelligent Information and Database Systems. Cham: Springer, 2022: 369-38. DOI: 10.1007/978-3-031-21967-2_30.
[38]
CUI S G, WEI M J, LIU C, et al. GAN-segNet: a deep generative adversarial segmentation network for brain tumor semantic segmentation[J]. Int J Imaging Syst Tech, 2022, 32(3): 857-868. DOI: 10.1002/ima.22677.
[39]
ZHU L, HE Q, HUANG Y, et al. DualMMP-GAN: dual-scale multi-modality perceptual generative adversarial network for medical image segmentation[J/OL]. Comput Biol Med, 2022, 144: 105387 [2023-04-25]. https://pubmed.ncbi.nlm.nih.gov/35305502/. DOI: 10.1016/j.compbiomed.2022.105387.
[40]
AZNI H M, AFSHARCHI M, ALLAHVERDI A. Improving brain tumor segmentation performance using CycleGAN based feature extraction[J]. Multimed Tools Appl, 2023, 82(12): 18039-18058. DOI: 10.1007/s11042-022-14174-3.
[41]
YOO J J, NAMDAR K, WAGNER M W, et al. A novel GAN-based paradigm for weakly supervised brain tumor segmentation of MR images[EB/OL]. 2022: arXiv: 2211.05269. https://arxiv.org/abs/2211.05269.pdf.
[42]
HAN K, WANG Y H, CHEN H T, et al. A survey on vision transformer[J]. IEEE Trans Pattern Anal Mach Intell, 2023, 45(1): 87-110. DOI: 10.1109/TPAMI.2022.3152247.
[43]
DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: transformers for image recognition at scale[EB/OL]. 2020: arXiv: 2010.11929. https://arxiv.org/abs/2010.11929.pdf
[44]
WANG W X, CHEN C, DING M, et al. TransBTS: multimodal brain tumor segmentation using transformer[M]//Medical Image Computing and Computer Assisted Intervention-MICCAI 2021. Cham: Springer International Publishing, 2021: 109-119. DOI: 10.1007/978-3-030-87193-2_11.
[45]
LI J Y, WANG W X, CHEN C, et al. TransBTSV2: towards better and more efficient volumetric segmentation of medical images[EB/OL]. 2022: arXiv: 2201.12785. https://arxiv.org/abs/2201.12785.pdf
[46]
LIU Z, LIN Y T, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). October 10-17, 2021, Montreal, QC, Canada. IEEE, 2022: 9992-10002. DOI: 10.1109/ICCV48922.2021.00986.
[47]
HATAMIZADEH A, TANG Y C, NATH V, et al. UNETR: transformers for 3D medical image segmentation[C]//2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). January 3-8, 2022, Waikoloa, HI, USA. IEEE, 2022: 1748-1758. DOI: 10.1109/WACV51458.2022.00181.
[48]
LIANG J J, YANG C H, ZENG L G. 3D PSwinBTS: an efficient transformer-based Unet using 3D parallel shifted windows for brain tumor segmentation[J/OL]. Digit Signal Process, 2022, 131: 103784 [2023-04-25]. https://www.sciencedirect.com/science/article/abs/pii/S1051200422004018?via%3Dihub. DOI: 10.1016/j.dsp.2022.103784.
[49]
JIANG Y, ZHANG Y, LIN X, et al. SwinBTS: a method for 3D multimodal brain tumor segmentation using swin transformer[J/OL]. Brain Sci, 2022, 12(6): 797 [2023-04-25]. https://pubmed.ncbi.nlm.nih.gov/35741682/. DOI: 10.3390/brainsci12060797.
[50]
LIANG J J, YANG C H, ZHONG J T, et al. BTSwin-unet: 3D U-shaped symmetrical swin transformer-based network for brain tumor segmentation with self-supervised pre-training[J]. Neural Process Lett, 2023, 55(4): 3695-3713. DOI: 10.1007/s11063-022-10919-1.
[51]
XING Z H, YU L Q, WAN L, et al. NestedFormer: nested modality-aware transformer for brain tumor segmentation[C]//WANG L, DOU Q, FLETCHER PT, et al. International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2022: 140-150. DOI: 10.1007/978-3-031-16443-9_14.
[52]
YANG Q S, GUO X Q, CHEN Z, et al. D2-net: dual disentanglement network for brain tumor segmentation with missing modalities[J]. IEEE Trans Med Imaging, 2022, 41(10): 2953-2964. DOI: 10.1109/TMI.2022.3175478.

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