MRI脑肿瘤图像分割研究进展及挑战

李锵; 白柯鑫; 赵柳; 关欣

doi:10.11834/jig.190524

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MRI脑肿瘤图像分割研究进展及挑战
Progresss and challenges of MRI brain tumor image segmentation
2020年25卷第3期页码：419-431
收稿：2019-10-18，

修回：2019-11-18，

录用：2019-11-25，

纸质出版：2020-03-16
DOI： 10.11834/jig.190524
稿件说明：

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李锵, 白柯鑫, 赵柳, 关欣. MRI脑肿瘤图像分割研究进展及挑战[J]. 中国图象图形学报, 2020,25(3):419-431. DOI： 10.11834/jig.190524.

Qiang Li, Kexin Bai, Liu Zhao, Xin Guan. Progresss and challenges of MRI brain tumor image segmentation[J]. Journal of Image and Graphics, 2020, 25(3): 419-431. DOI： 10.11834/jig.190524.

摘要

脑肿瘤分割是医学图像处理中的一项重要内容，其目的是辅助医生做出准确的诊断和治疗，在临床脑部医学领域具有重要的实用价值。核磁共振成像（MRI）是临床医生研究脑部组织结构的主要影像学工具，为了使更多研究者对MRI脑肿瘤图像分割理论及其发展进行探索，本文对该领域研究现状进行综述。首先总结了用于MRI脑肿瘤图像分割的方法，并对现有方法进行了分类，即分为监督分割和非监督分割；然后重点综述了基于深度学习的脑肿瘤分割方法，在研究其关键技术基础上归纳了优化策略；最后介绍了脑肿瘤分割（BraTS）挑战，并结合挑战中所用方法展望了脑肿瘤分割领域未来的发展趋势。MRI脑肿瘤图像分割领域的研究已经取得了一些显著进展，尤其是深度学习的发展为该领域的研究提供了新的思路。但由于脑肿瘤在大小、形状和位置方面的高度变化，以及脑肿瘤图像数据有限且类别不平衡等问题，使得脑肿瘤图像分割仍是一个极具挑战的课题。由于分割过程缺乏可解释性和透明性，如何将全自动分割方法应用于临床试验，还需要进行深入研究。

Abstract

Brain tumor segmentation is an important part of medical image processing. It assists doctors in making accurate diagnoses and treatment plans. It clearly carries important practical value in clinical brain medicine. With the development of medical imaging

imaging technology plays an important role in the evaluation of the treatment of brain tumor patients and can provide doctors with a clear internal structure of the human body. Magnetic resonance imaging (MRI) is the main imaging tool for clinicians to study the structure of brain tissue. Brain tumor MRI modalities include T1-weighted

contrast-enhanced T1-weighted

T2-weighted

and liquid attenuation inversion recovery pulses. Different imaging modalities can provide complementary information to analyze brain tumors. These four types of modalities are usually combined to diagnose the location and size of brain tumors. At present

due to the extensive application of MRI equipment in brain examination

a large number of brain MRI images are generated in the clinical setting. This rise in the quantity of brain MRI images hinders doctors in manually annotating and segmenting all images promptly. Moreover

the manual segmentation of brain tumor tissues highly depends on doctors' professional experience. Therefore

research has focused on the ways to segment brain tumors efficiently

accurately

and automatically. In recent years

significant advancement has been made in the study of brain tumor segmentation methods. To enable other researchers to explore the theory and development of segmentation methods for brain tumor MRI images

this work reviews the current research status in this field. In this study

the current semiautomatic and fully automatic segmentation methods for brain tumor MRI images are divided into two categories:unsupervised segmentation and supervised segmentation. The difference between the two methods lies in the use of hand-labeled image data. Unsupervised segmentation is a nonpriori image segmentation method based on computer clustering statistical analysis. The unsupervised methods are divided into threshold-based

region-based

pixel-based

and model-based segmentation technologies according to the different segmentation principles. This work briefly describes the unsupervised methods according to the above classification and summarizes their advantages and disadvantages. The main feature of supervised segmentation is its use of labeled image data. The segmentation process involves model training and testing. In the former

labeled data are used to learn the mapping from image features to labels. In the latter

the model assigns labels to unlabeled data. Supervised segmentationis mainly based on the segmentation technology of pixel classification. It generally includes traditional machine learning methods and methods based on neural networks. The common traditional machine learning methods and the methods based on neural networks used in brain tumor segmentation are briefly described herein

and their advantages and disadvantages are summarized. The segmentation methods for brain tumor images based on deep learning are mainly described. With the advancement of artificial intelligence

deep learning

especially the new technology represented by convolutional neural networks (CNNs)

has been well received because of its superior brain tumor segmentation results. Compared with traditional segmentation methods

CNNs can automatically learn representative complex features directly from the data. Hence

the research of brain tumor segmentation based on CNNs mainly focuses on network structure design rather than image processing before feature extraction. This study focuses on the structure of neural networks used in the field of brain tumor image segmentation and summarizes the optimization strategies of deep learning. Lastly

the challenge of brain tumor segmentation (BraTS) is introduced

and the future development trend of brain tumor segmentation is established in combination with the methods used in the challenge. The BraTS challenge is a competition to evaluate the segmentation methods for brain tumor MRI images.The BraTS challenge uses preoperative MRI image data from multiple institutional brains to focus on the segmentation of gliomas. In addition

the BraTS challenge involves predicting overall patient survival by combining radiological features with machine learning algorithms to determine the clinical relevance of this segmentation task. The segmentation methods for brain tumor MRI images have inherent advantages

disadvantages

and application scope. Researchers have been working on how to improve the accuracy of segmentation results

the robustness of models

and the overall operational efficiency. Hence

this study analyzes the advantages and disadvantages of various methods

the optimization strategies of deep learning

and future development trends. The optimization strategy of deep learning is as follows.In the aspect of imaging data

data enhancement techniques

such as flipping

scaling

and cropping

are used to increase the amount of training data and improve the generalization ability of models. Acascade framework is introduced to realize the segmentation of whole tumors

core tumors

and enhanced tumors by combining the acascade framework with the inclusion relationship of tumors in the brain anatomical structure. An improved loss function is used to deal with image category imbalances. In terms of network structure

multiscale and multichannel strategies are adopted to make full use of image feature information.In the process of downsampling

aconvolution operation is used instead of pooling so that image information can be further learned while reducing image information loss.Between the convolutional layers

the jump connection method is applied to effectively solve the degradation problem of the deep network.In different cases

the appropriate standardization method

activation function

and loss rate are selected to achieve satisfactory segmentation effects. This work summarizes the development trend of brain tumor segmentation methods by learning and arranging the methods used in the BraTS challenge. As a result of the diversification of MRI imaging modalities

making full use of the each modal image information can effectively improve the accuracy of brain tumor segmentation. Therefore

the reasonable utilization of multimodality images can be expected to become a research hotspot.Methods based on deep learning are outstanding in the field of brain tumor segmentation and have become a hot research direction.The defects of machine learning algorithms lead to an inaccurate segmentation of brain tumors. A popular trend is to improve the original method or combine various methods effectively. Remarkable progress has been made in the segmentation of brain tumor MRI images. The development of deep learning

in particular

provides new ideas for the research in this field. However

brain tumor image segmentation is still a challenging subject because brain tumors vary in size

shape

and position. Moreover

brain tumor image data are limited

and the categories are not balanced. As a result of the lack of interpretability and transparency in the segmentation process

the application of a fully automated segmentation method to clinical trials still requires further research.

关键词

Keywords

references

Bauer S, Nolte L P and Reyes M. 2011a. Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization//Proceedings of the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention. Toronto: Springer: 354-361[ DOI:10.1007/978-3-642-23626-6_44 http://dx.doi.org/10.1007/978-3-642-23626-6_44 ]

Bauer S, Nolte L P and Reyes M. 2011b. Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model//Proceedings of 2011 IEEE International Symposium on Biomedical Imaging: from Nano to Macro. Chicago: IEEE: 2018-2021[ DOI:10.1109/ISBI.2011.5872808 http://dx.doi.org/10.1109/ISBI.2011.5872808 ]

Bauer S, Wiest R, Nolte L P and Reyes M. 2013. A survey of MRI-based medical image analysis for brain tumor studies. Physics in Medicine and Biology, 58(13):R97-R129[DOI:10.1088/0031-9155/58/13/R97]

Benson C C, Lajish V L and RajamaniK. 2015. Brain tumor extraction from MRI brain images using marker based watershed algorithm//Proceedings of 2015 International Conference on Advances in Computing, Communications and Informatics. Kochi, India: IEEE: 318-323[ DOI:10.1109/ICACCI.2015.7275628 http://dx.doi.org/10.1109/ICACCI.2015.7275628 ]

Bezdek J C, Hall L O and Clarke L P. 1993. Review of MR image segmentation techniques using pattern recognition. Medical Physics, 20(4):1033-1048[DOI:10.1118/1.597000]

Bray F, Ferlay J, Soerjomataram I, Siegel R L, Torre L A and Jemal A. 2018. Global cancer statistics 2018:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA:A Cancer Journal for Clinicians, 68(6):394-424[DOI:10.3322/caac.21492]

Cai H M, Verma R, Ou Y M, Lee S K, Melhem E R and Davatzikos C. 2007. Probabilistic segmentation of brain tumors based on multi-modality magnetic resonance images//Proceedings of the 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Arlington: IEEE: 600-603[ DOI:10.1109/ISBI.2007.356923 http://dx.doi.org/10.1109/ISBI.2007.356923 ]

Capelle A S, Colot O and Fernandez-Maloigne C. 2004. Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information. Information Fusion, 5(3):203-216[DOI:10.1016/j.inffus.2003.10.001]

Casamitjana A, CatàM, Sánchez I, Combalia M and Vilaplana V. 2017. Cascaded V-Net using ROI masks for brain tumor segmentation//Proceedings of the 3rd International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City, QC, Canada: Springer: 381-391[ DOI:10.1007/978-3-319-75238-9_33 http://dx.doi.org/10.1007/978-3-319-75238-9_33 ]

Chen H, Dou Q, Yu L Q and Heng P A. 2016. VoxResNet: deep voxelwise residual networks for volumetric brain segmentation[EB/OL].[2019-10-03] . https://arxiv.org/pdf/1608.05895.pdf https://arxiv.org/pdf/1608.05895.pdf

Chen L L, Wu Y, DSouza A M, Abidin A Z, Wismüller A and Xu C L. 2018. MRI tumor segmentation with densely connected 3D CNN//Proceedings Volume 10574, Medical Imaging 2018: Image Processing. Houston: SPIE: 10574[ DOI:10.1117/12.2293394 http://dx.doi.org/10.1117/12.2293394 ]

Corso J J, Sharon E, Dube S, El-Saden S, Sinha U and Yuille A. 2008. Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Transactions on Medical Imaging, 27(5):629-640[DOI:10.1109/TMI.2007.912817]

Dam E, Loog M and Letteboer M. 2004. Integrating automatic and interactive brain tumor segmentation//Proceedings of the 17th International Conference on Pattern Recognition. Cambridge: IEEE: 790-793[ DOI:10.1109/ICPR.2004.1334647 http://dx.doi.org/10.1109/ICPR.2004.1334647 ]

DeAngelis L M. 2001. Brain tumors. NewEngland Journal of Medicine, 344(2):114-123[DOI:10.1056/NEJM200101113440207]

Deng W K, Xiao W, Deng H and Liu J G. 2010. MRI brain tumor segmentation with region growing method based on the gradients and variances along and inside of the boundary curve//Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics. Yantai: IEEE: 393-396[ DOI:10.1109/BMEI.2010.5639536 http://dx.doi.org/10.1109/BMEI.2010.5639536 ]

Dong H, Yang G, Liu F D, Mo Y H and Guo Y K. 2017. Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks//Proceedings of the 21st Annual Conference on Medical Image Understanding and Analysis. Edinburgh: Springer: 506-517[ DOI:10.1007/978-3-319-60964-5_44 http://dx.doi.org/10.1007/978-3-319-60964-5_44 ]

Dvořák P and Menze B H. 2015. Structured prediction with convolutional neural networks for multimodal brain tumor segmentation//Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge. Munich: MICCAI: 13-24

Farag A A, Ahmed M N, El-Baz A and Hassan H. 2005. Advanced segmentation techniques//Suri J S, Wilson D L and Laxminarayan S, eds. Handbook of Biomedical Image Analysis: Volume Ⅰ: Segmentation Models Part A. Boston: Springer: 479-533[ DOI:10.1007/0-306-48551-6_9 http://dx.doi.org/10.1007/0-306-48551-6_9 ]

Gibbs P, Buckley D L, Blackband S J and Horsman A. 1996. Tumour volume determination from MR images by morphological segmentation. Physics in Medicine and Biology, 41(11):2437-2446[DOI:10.1088/0031-9155/41/11/014]

Gies V and Bernard T M. 2004. Statistical solution to watershed over-segmentation//Proceedings of 2004 International Conference on Image Processing. Singapore: IEEE: 1863-1866[ DOI:10.1109/ICIP.2004.1421440 http://dx.doi.org/10.1109/ICIP.2004.1421440 ]

Gordillo N, Montseny E and Sobrevilla P. 2013. State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8):1426-1438[DOI:10.1016/j.mri.2013.05.002]

Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P M and Larochelle H. 2017. Brain tumor segmentation with deep neural networks. Medical Image Analysis, 35:18-31[DOI:10.1016/j.media.2016.05.004]

He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 770-778[ DOI:10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]

Ho S, Bullitt E and Gerig G. 2002. Level-set evolution with region competition: automatic 3-D segmentation of brain tumors//Proceedings of the 16th International Conference on Pattern Recognition. Washington: IEEE: 10532

Hou Z J. 2006. A review on MR image intensity inhomogeneity correction. International Journal of Biomedical Imaging, 2006:49515[DOI:10.1155/IJBI/2006/49515]

Huang G, Liu Z, van der Maaten L and Weinberger K Q. 2017. Densely connected convolutional networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE: 4700-4708[ DOI:10.1109/CVPR.2017.243 http://dx.doi.org/10.1109/CVPR.2017.243 ]

Iftekharuddin K M, Zheng J, Islam M A and Ogg R J. 2009. Fractal-based brain tumor detection in multimodal MRI. Applied Mathematics and Computation, 207(1):23-41[DOI:10.1016/j.amc.2007.10.063]

Isensee F, Kickingereder P, Wick W, Bendszus M and Maier-Hein K H. 2017. Brain tumor segmentation and radiomics survival prediction: contribution to the brats 2017 challenge//Proceedings of the 3rd International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City: Springer: 287-297[ DOI:10.1007/978-3-319-75238-9_25 http://dx.doi.org/10.1007/978-3-319-75238-9_25 ]

Işın A, Direkoǧlu C and Şah M. 2016. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science, 102:317-324[DOI:10.1016/j.procs.2016.09.407]

Jesson A and Arbel T. 2017. Brain tumor segmentation using a 3D FCN with multi-scale loss//Proceedings of the 3rd International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City: Springer: 392-402[ DOI:10.1007/978-3-319-75238-9_34 http://dx.doi.org/10.1007/978-3-319-75238-9_34 ]

Kamnitsas K, Bai W, Ferrante E, McDonagh S, Sinclair M, Pawlowski N, Rajchl M, Lee M, Kainz B, Rueckert D and Glocker B. 2017a. Ensembles of multiple models and architectures for robust brain tumour segmentation//Proceedings of the 3rd International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City: Springer: 450-462[ DOI:10.1007/978-3-319-75238-9_38 http://dx.doi.org/10.1007/978-3-319-75238-9_38 ]

Kamnitsas K, Ledig C, Newcombe V F J, Simpson J P, Kane A D, Menon D K, Rueckert D and Glocker B. 2017b. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36:61-78[DOI:10.1016/j.media.2016.10.004]

Kannan S R. 2008. A new segmentation system for brain MR images based on fuzzy techniques. Applied Soft Computing, 8(4):1599-1606[DOI:10.1016/j.asoc.2007.10.025]

Krell M M, Seeland A and Kim S K. 2018. Data augmentation for brain-computer interfaces: analysis on event-related potentials data[EB/OL].[2019-10-03] . https://arxiv.org/pdf/1801.02730.pdf https://arxiv.org/pdf/1801.02730.pdf

Lachinov D, Vasiliev E and Turlapov V. 2018. Glioma segmentation with cascaded UNet//Proceedings of the 4th International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Granada: Springer: 189-198[ DOI:10.1007/978-3-030-11726-9_17 http://dx.doi.org/10.1007/978-3-030-11726-9_17 ]

Lefkovits L, Lefkovits S and Szilágyi L. 2016. Brain tumor segmentation with optimized random forest//Proceedings of the 2nd International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Athens: Springer: 88-99[ DOI:10.1007/978-3-319-55524-9_9 http://dx.doi.org/10.1007/978-3-319-55524-9_9 ]

Letteboer M M, Olsen O F, Dam E B, Willems P W A, Viergever M A and Niessen W J. 2004. Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm. Academic Radiology, 11(10):1125-1138[DOI:10.1016/j.acra.2004.05.020]

Liang Z P and Lauterbur P C. 2000. Principles of Magnetic Resonance Imaging:A Signal Processing Perspective. New York:The Institute of Electrical and Electronics Engineers Press

Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of 2015 IEEE Computer Vision and Pattern Recognition. Boston: IEEE: 3431-3440[ DOI:10.1109/CVPR.2015.7298965 http://dx.doi.org/10.1109/CVPR.2015.7298965 ]

Luo S H, Li R X and Ourselin S. 2003. A new deformable model using dynamic gradient vector flow and adaptive balloon forces//APRS Workshop on Digital Computing. Brisbane, Australia: [s.n.]: 9-14

Madabhushi A and Udupa J K. 2005. Interplay between intensity standardization and inhomogeneity correction in MR image processing. IEEE Transactions on Medical Imaging, 24(5):561-576[DOI:10.1109/TMI.2004.843256]

Malladi R, Sethian J A and Vemuri B C. 1995. Shape modeling with front propagation:a level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(2):158-175[DOI:10.1109/34.368173]

McInerney T and Terzopoulos D. 1996. Deformable models in medical image analysis//Proceedings of Workshop on Mathematical Methods in Biomedical Image Analysis. San Francisco: IEEE: 171-180[ DOI:10.1109/MMBIA.1996.534069 http://dx.doi.org/10.1109/MMBIA.1996.534069 ]

Mok T C W and Chung A C S. 2018. Learning data augmentation for brain tumor segmentation with coarse-to-fine generative adversarial networks//Proceedings of the 4th International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Granada: Springer: 70-80[ DOI:10.1007/978-3-030-11723-8_7 http://dx.doi.org/10.1007/978-3-030-11723-8_7 ]

Nie J X, Xue Z, Liu T M, Young G S, Setayesh K, Guo L and Wong S T C. 2009. Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field. Computerized Medical Imaging and Graphics, 33(6):431-441[DOI:10.1016/j.compmedimag.2009.04.006]

Olabarriaga S D and Smeulders A W M. 2001. Interaction in the segmentation of medical images:a survey. Medical Image Analysis, 5(2):127-142[DOI:10.1016/S1361-8415(00)00041-4]

Papageorgiou E I, Spyridonos P P, Glotsos D T, Stylios C D, Ravazoula P, Nikiforidis G N and Groumpos P P. 2008. Brain tumor characterization using the soft computing technique of fuzzy cognitive maps. Applied Soft Computing, 8(1):820-828[DOI:10.1016/j.asoc.2007.06.006]

Pereira S, Pinto A, Alves V and Silva C A. 2016. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5):1240-1251[DOI:10.1109/TMI.2016.2538465]

Pham D L, Xu C Y and Prince J L. 2000. Current methods in medical image segmentation. Annual Review of Biomedical Engineering, 2:315-337[DOI:10.1146/annurev.bioeng.2.1.315]

Pinto A, Pereira S, Dinis H, Silva C A and Rasteiro D M L D. 2015. Random decision forests for automatic brain tumor segmentation on multi-modal MRI images//Proceedings of the 4th Portuguese Meeting on Bioengineering. Porto: IEEE: 1-5[ DOI:10.1109/ENBENG.2015.7088842 http://dx.doi.org/10.1109/ENBENG.2015.7088842 ]

Prastawa M, Bullitt E, Ho S and Gerig G. 2004. A brain tumor segmentation framework based on outlier detection. Medical Image Analysis, 8(3):275-283[DOI:10.1016/j.media.2004.06.007]

Rao C H, Naganjaneyulu P V and Prasad K S. 2017. Brain tumor detection and segmentation using conditional random field//Proceedings of the 7th International Advance Computing Conference. Hyderabad: IEEE: 807-810[ DOI:10.1109/IACC.2017.0166 http://dx.doi.org/10.1109/IACC.2017.0166 ]

Ren L, Li Q, Guan X and Ma J. 2018. Three-dimensional segmentation of brain tumors in magnetic resonance imaging based on improved continuous max-flow.Laser&Optoelectronics Progress, 55(11):215-223

任璐, 李锵, 关欣, 马杰. 2018.改进的连续型最大流算法脑肿瘤磁核共振成像三维分割.激光与光电子学进展, 55(11):215-223)[DOI:10.3788/LOP55.111011]

Rexilius J, Hahn H K, Klein J, Lentschig M G and Peitgen H O. 2007. Multispectral brain tumor segmentation based on histogram model adaptation//Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis. San Diego: SPIE, 6514: 65140V[ DOI:10.1117/12.709410 http://dx.doi.org/10.1117/12.709410 ]

Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer: 234-241[ DOI:10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]

Ruan S, Lebonvallet S, Merabet A and Constans J. 2007. Tumor segmentation from a multispectral MRI images by using support vector machine classification//Proceedings of the 4th IEEE International Symposium on Biomedical Imaging: from Nano to Macro. Arlington: IEEE: 1236-1239[ DOI:10.1109/ISBI.2007.357082 http://dx.doi.org/10.1109/ISBI.2007.357082 ]

Ruan S, Zhang N, Liao Q M and Zhu Y M. 2011. Image fusion for following-up brain tumor evolution//Proceedings of 2011 IEEE International Symposium on Biomedical Imaging: from Nano to Macro. Chicago: IEEE: 281-284[ DOI:10.1109/ISBI.2011.5872406 http://dx.doi.org/10.1109/ISBI.2011.5872406 ]

Sato M, Lakare S, Wan M, Kaufman A and Nakajima M. 2000. A gradient magnitude based region growing algorithm for accurate segmentation//Proceedings 2000 International Conference on Image Processing. Vancouver: IEEE: 448-451[ DOI:10.1109/ICIP.2000.899432 http://dx.doi.org/10.1109/ICIP.2000.899432 ]

Schwartzbaum J A, Fisher J L, Aldape K D and Wrensch M. 2006. Epidemiology and molecular pathology of glioma. Nature Clinical Practice Neurology, 2(9):494-503[DOI:10.1038/ncpneuro0289]

Shen T, Huang X L, Li H S, Kim E, Zhang S T and Huang J Z. 2011. A 3D Laplacian-driven parametric deformable model//Proceedings of 2011 IEEE International Conference on Computer Vision. Barcelona: IEEE: 279-286[ DOI:10.1109/ICCV.2011.6126253 http://dx.doi.org/10.1109/ICCV.2011.6126253 ]

Sherman R. 2018. A volumetric convolutional neural network for brain tumor segmentation[EB/OL].[2019-10-03] . https://arxiv.org/pdf/1811.0265401.pdf https://arxiv.org/pdf/1811.0265401.pdf

Shi D L, Li Q and Guan X. 2018. Brain tumor image segmentation algorithm based on convolution neural network and fuzzy inference system.Journal of Frontiers of Computer Science and Technology, 12(4):608-617

师冬丽, 李锵, 关欣. 2018.结合卷积神经网络和模糊系统的脑肿瘤分割.计算机科学与探索, 12(4):608-617)[DOI:10.3778/j.issn.1673-9418.1704042]

Siegel R, Naishadham D and Jemal A. 2013. Cancer statistics, 2013. CA:A Cancer Journal for Clinicians, 63(1):11-30[DOI:10.3322/caac.21166]

Stadlbauer A, Moser E, Gruber S, Buslei R, Nimsky C, Fahlbusch R and Ganslandt O. 2004. Improved delineation of brain tumors:an automated method for segmentation based on pathologic changes of 1H-MRSI metabolites in gliomas. NeuroImage, 23(2):454-461[DOI:10.1016/j.neuroimage.2004.06.022]

Stawiaski J. 2017. A multiscale patch based convolutional network for brain tumor segmentation[EB/OL].[2019-10-03] . https://arxiv.org/pdf/1710.0231601.pdf https://arxiv.org/pdf/1710.0231601.pdf

Sung Y C, Han K S, Song C J, Noh SM and Park J W. 2000. Threshold estimation for region segmentation on MR image of brain having the partial volume artifact//Proceedings of the 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000. Beijing: IEEE: 1000-1009[ DOI:10.1109/ICOSP.2000.891695 http://dx.doi.org/10.1109/ICOSP.2000.891695 ]

Tek H and Kimia B B. 1995. Shock-based reaction-diffusion bubbles for image segmentation//Proceedings of the 1st International Conference on Computer Vision, Virtual Reality and Robotics in Medicine. Nice: Springer: 434-438[ DOI:10.1007/978-3-540-49197-2_55 http://dx.doi.org/10.1007/978-3-540-49197-2_55 ]

Tong Y F, Li Q and Guan X. 2018. An improved multi-modal brain tumor segmentation hybrid algorithm. Journal of Signal Processing, 34(3):340-346

童云飞, 李锵, 关欣. 2018.改进的多模式脑肿瘤图像混合分割算法.信号处理, 34(3):340-346)[DOI:10.16798/j.issn.1003-0530.2018.03.011]

Vapnik V. 2000. The Nature of Statistical Learning Theory. New York:Springer

Vovk U, Pernus F and Likar B. 2007. A review of methods for correction of intensity inhomogeneity in MRI. IEEE Transactions on Medical Imaging, 26(3):405-421[DOI:10.1109/TMI.2006.891486]

Wang G T, Li W Q, Ourselin S and Vercauteren T. 2017. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks//Proceedings of the 3rd International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City: Springer: 178-190[ DOI:10.1007/978-3-319-75238-9_16 http://dx.doi.org/10.1007/978-3-319-75238-9_16 ]

Węgliński T and Fabijańska A. 2011. Brain tumor segmentation from MRI data sets using region growing approach//Perspective Technologies and Methods in MEMS Design. Polyana: IEEE: 185-188

Wong K P. 2005. Medical image segmentation: methods and applications in functional imaging//Suri J S, Wilson D L and Laxminarayan S, eds. Handbook of Biomedical Image Analysis: Volume Ⅱ: Segmentation Models Part B. Boston: Springer: 111-182[ DOI:10.1007/0-306-48606-7_3 http://dx.doi.org/10.1007/0-306-48606-7_3 ]

Wu W, Chen A Y C, Zhao L and Corso J J. 2014. Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. International Journal of Computer Assisted Radiology and Surgery, 9(2):241-253[DOI:10.1007/s11548-013-0922-7]

Xing B T. 2018. MR Brain Tumor Image Segmentation Algorithm Based on Fully Convolutional Neural Network. Tianjin:Tianjin University

邢波涛. 2018.基于全卷积神经网络的MR脑肿瘤图像分割算法研究.天津:天津大学

Zhang J C, Shen X L, Zhuo T Q and Zhou H. 2017. Brain tumor segmentation based on refined fully convolutional neural networks with a hierarchical dice loss[EB/OL].[2019-10-03] . https://arxiv.org/pdf/1712.09093.pdf https://arxiv.org/pdf/1712.09093.pdf

Zhao L Y and Jia K B. 2016. Multiscale CNNs for brain tumor segmentation and diagnosis. Computational and Mathematical Methods in Medicine, 2016:8356294[DOI:10.1155/2016/8356294]

Zhao X M, Wu Y H, Song G D, Li Z Y, Zhang Y Z and Fan Y. 2018. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical Image Analysis, 43:98-111[DOI:10.1016/j.media.2017.10.002]