浏览全部资源
扫码关注微信
纸质出版日期： 2020-02-16 ，
录用日期： 2019-08-05
移动端阅览
江宗康, 吕晓钢, 张建新, 张强, 魏小鹏. MRI脑肿瘤图像分割的深度学习方法综述[J]. 中国图象图形学报, 2020,25(2):215-228.
Zongkang Jiang, Xiaogang Lyu, Jianxin Zhang, Qiang Zhang, Xiaopeng Wei. Review of deep learning methods for MRI brain tumor image segmentation[J]. Journal of Image and Graphics, 2020,25(2):215-228.
磁共振成像（MRI）作为一种典型的非侵入式成像技术，可产生高质量的无损伤和无颅骨伪影的脑影像，为脑肿瘤的诊断和治疗提供更为全面的信息，是脑肿瘤诊疗的主要技术手段。MRI脑肿瘤自动分割利用计算机技术从多模态脑影像中自动将肿瘤区（坏死区、水肿区、非增强肿瘤区和增强肿瘤区）和正常组织区进行分割和标注，对于辅助脑肿瘤的诊疗具有重要作用。本文对MRI脑肿瘤图像分割的深度学习方法进行了总结与分析，给出了各类方法的基本思想、网络架构形式、代表性改进方案以及优缺点总结等，并给出了部分典型方法在BraTS（multimodal brain tumor segmentation）数据集上的性能表现与分析结果。通过对该领域研究方法进行综述，对现有基于深度学习的MRI脑肿瘤分割研究方法进行了梳理，作为新的发展方向，MRI脑肿瘤图像分割的深度学习方法较传统方法已取得明显的性能提升，已成为领域主流方法并持续展现出良好的发展前景，有助于进一步推动MRI脑肿瘤分割在临床诊疗上的应用。
Brain tumors
abnormal cells growing in the human brain
are common neurological diseases that are extremely harmful to human health. Malignant brain tumors can lead to high mortality. Magnetic resonance imaging (MRI)
a typical noninvasive imaging technology
can produce high-quality brain images without damage and skull artifacts
as well as provide comprehensive information to facilitate the diagnosis and treatment of brain tumors. Additionally
the segmentation of MRI brain tumors utilizes computer technology to segment and label tumors (necrosis
edema
and nonenhanced and enhanced tumors) and normal tissues automatically on multimodal brain images
which assists in their diagnosis and treatment. However
given the complexity of brain tissue structure
the diversity of spatial location
the shape and size of brain tumors
and various influence factors
such as field offset effect
volume effect
and equipment noise
during the processing of MRI brain images
automatically achieving accurate tumor segmentation results from MRI brain images has been challenging. With the continuous breakthroughs of deep learning technology in computer vision and medical image analysis
MRI brain tumor segmentation methods based on deep learning have also attracted wide attention in recent years. A series of important research results have been reported
illuminating the promising potential of deep learning methods for MRI brain tumor segmentation task. Therefore
this work aims to review deep learning-based MRI brain tumor segmentation methods
i.e.
the current mainstream of MRI brain tumor segmentation. Through an extensive study of the literature on MRI brain tumor segmentation problem
we comprehensively summarize and analyze the existing deep learning methods for MRI brain tumor segmentation. To provide a further understanding of this task
we first introduce a family of authoritative brain tumor segmentation databases
i.e.
BraTS (2012-2018) Databases
which run in conjunction with the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2012-2018 Conferences. Several important evaluation metrics
including dice similarity coefficient
predictive positivity value
and sensitivity
are also briefly described. On the basis of the basic network architecture for brain tumor segmentation
we classify the existing deep learning-based MRI brain tumor segmentation methods into three categories
namely
convolutional neural network-
fully convolutional network-
and generative adversarial network-based MRI brain tumor segmentation methods. Convolutional neural network-based methods can be further divided into three sub-categories:single network-based
multinetwork-based
and traditional-method-combination-based approaches. On the basis of the three categories
we comprehensively describe and analyze the basic ideas
network architecture
and typical improvement schemes for each type of method. In addition
we compare the performance results of the representative methods achieved on the BraTS series datasets and summarize the comparative analysis results as well as the advantages and disadvantages of the representative methods. Finally
we discuss three possible future research directions.By reviewing the main work in this field
the existing deep learning methods for MRI brain tumor segmentation are examined well
and our threefold conclusion follows:1) Embedding advanced network architecture or introducing prior information of brain tumors into the deep segmentation network will achieve superior accuracy performance for each type of method. 2) Fully convolutional network-based MRI brain tumor segmentation methods can obtain improved balance between accuracy and efficiency. 3) Generative adversarial network-based MRI brain tumor segmentation methods
a novel and powerful semi-supervised method
has shown good potential for the extremely challenging construction of a large-scale MRI brain tumor segmentation dataset with fine labels. Three possible future research directions are recommended
namely
embedding numerous powerful feature representation modules (e.g.
squeeze-and-excitation block
matrix power normalization unit)
constructing semi-supervised networks with prior medical knowledge (e.g.
constraint information
location
and size and shape information of brain tumors)
and transferring networks from other image tasks (e.g.
promising detection networks of faster and masker region-based convolutional neural networks). MRI brain tumor segmentation is an important step in the diagnosis and treatment of brain tumors. This process can quickly obtain further accurate MRI brain tumor segmentation results through computer technology
which can effectively assist doctors in computing the location and size of tumors and formulating numerous reasonable treatment and rehabilitation strategies for patients with brain tumors. As a new development direction in recent years
deep learning-based MRI brain tumor segmentation has achieved significant performance improvement over traditional methods. As the mainstream in this field
this method will further promote the clinical diagnosis and treatment level of computer-aided MRI brain tumor segmentation technology.
磁共振成像脑肿瘤人工神经网络深度学习分割
magnetic resonance imaging(MRI)brain tumorartificial neural networksdeep learningsegmentation
Beers A, Chang K, Brown J, Sartor E, Mammen C P, Gerstner E, Rosen B and Kalpathy-Cramer J. 2017. Sequential 3D U-Nets for Biologically-informed brain tumor segmentation[EB/OL].[2019-07-27].https://arxiv.org/pdf/1709.02967.pdfhttps://arxiv.org/pdf/1709.02967.pdf
Ben Naceur M, Saouli R, Akil M and Kachouri R. 2018. Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in MRI images. Computer Methods and Programs in Biomedicine, 166:39-49[DOI:10.1016/j.cmpb. 2018.09.007]
Casamitjana A, Puch S, Aduriz A and Vilaplana V. 2016. 3D Convolutional Neural Networks for Brain Tumor Segmentation: a comparison of multi-resolution architectures//Proceedings of the 2nd International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Athens, Greece: Springer: 150-161[DOI:10.1007/978-3-319-55524-9_15http://dx.doi.org/10.1007/978-3-319-55524-9_15]
Chen H, Dou Q, Yu L Q, Qin J and Heng P A. 2018a. VoxResNet:deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage, 170:446-455[DOI:10.1016/j.neuroimage.2017.04.041]
Chen L L, Wu Y, Dsouza A M, Abidin A Z, Wismüller A and Xu C L. 2018b. MRI tumor segmentation with densely connected 3D CNN//Proceedings of SPIE 10571, Medical Imaging 2018: Image Processing. Houston, USA: SPIE: 105741F[DOI:10.1117/12.2293394http://dx.doi.org/10.1117/12.2293394]
ÇiçekÖ, Abdulkadir A, Lienkamp S S, Brox T and Ronneberger O. 2016. 3D U-Net: learning dense volumetric segmentation from sparse annotation//Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention. Athens, Greece: Springer: 424-432[DOI:10.1007/978-3-319-46723-8_49http://dx.doi.org/10.1007/978-3-319-46723-8_49]
Colmeiro R G R, Verrastro C A and Grosges T. 2018. Multimodal brain tumor segmentation using 3D convolutional networks//Proceedings of the 3rd International MICCAI Brainlesion Workshop. Quebec City, Canada: Springer: 226-240[DOI:10.1007/978-3-319-75238-9_20http://dx.doi.org/10.1007/978-3-319-75238-9_20]
Cui Z P, Yang J and Qiao Y. 2016. Brain MRI segmentation with patch-based CNN approach//Proceedings of the 35th Chinese Control Conference. Chengdu, China: IEEE: 7026-7031[DOI:10.1109/ChiCC.2016.7554465http://dx.doi.org/10.1109/ChiCC.2016.7554465]
Deng W K. 2011. A Study on MRI Brain Tumor Segmentation Approach. Wuhan:Huazhong University of Science and Technology
邓万凯. 2011. MRI图像的脑肿瘤分割方法研究.武汉:华中科技大学[DOI:10.7666/d.d201431]
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, United Kingdom: Springer: 506-517[DOI:10.1007/978-3-319-60964-5_44http://dx.doi.org/10.1007/978-3-319-60964-5_44]
Dvořák P and Menze B. 2016. Local structure prediction with convolutional neural networks for multimodal brain tumor segmentation//Proceedings of International MICCAI Workshop on Medical Computer Vision. Munich, Germany: Springer: 59-71[DOI:10.1007/978-3-319-42016-5_6http://dx.doi.org/10.1007/978-3-319-42016-5_6]
Erden B, Gamboa N and Wood S. 2017. 3D convolutional neural network for brain tumor segmentation[EB/OL].[2019-07-27].http://cs231n.stanford.edu/reports/2017/pdfs/526.pdfhttp://cs231n.stanford.edu/reports/2017/pdfs/526.pdf
Esteva A, Kuprel B, Novoa R A, Ko J, Swetter S M, Blau H M and Thrun S. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639):115-118[DOI:10.1038/nature21056]
Feng X, Tustison N and Meyer C. 2018. Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features//Proceedings of the 4th International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Granada, Spain: Springer: 279-288[DOI:10.1007/978-3-030-11726-9_25http://dx.doi.org/10.1007/978-3-030-11726-9_25]
Ge T, Mu N and Li L. 2017. A Brain tumor segmentation method based on softmax regression and graph cut. Acta Electronica Sinica, 45(3):644-649
葛婷, 牟宁, 李黎. 2017.基于softmax回归与图割法的脑肿瘤分割算法.电子学报, 45(3):644-649[DOI:10.3969/j.issn.0372-2112.2017.03.021]
Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y. 2014. Generative adversarial nets//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press: 2672-2680
Gulshan V, Peng L, Coram M, Stumpe M C, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson P C, Mega J L and Webster D R. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22):2402-2410[DOI:10.1001/jama.2016.17216]
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, Gkioxari G, Dollár P and Girshick R. 2017. Mask R-CNN//Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2980-2988[DOI:10.1109/ICCV.2017.322http://dx.doi.org/10.1109/ICCV.2017.322]
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 770-778[DOI:10.1109/CVPR.2016.90http://dx.doi.org/10.1109/CVPR.2016.90]
Hu J, Shen L and Sun G. 2018. Squeeze-and-excitation networks//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 7132-7141[DOI:10.1109/CVPR.2018.00745http://dx.doi.org/10.1109/CVPR.2018.00745]
Huang G, Liu Z, Van Der Maaten L and Weinberger K Q. 2017. Densely connected convolutional networks//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu Hawaii, USA: IEEE: 2261-2269[DOI:10.1109/CVPR.2017.243http://dx.doi.org/10.1109/CVPR.2017.243]
Hussain S, Anwar S M and Majid M. 2017. Brain tumor segmentation using cascaded deep convolutional neural network//Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Seogwipo, South Korea: IEEE:1998-2001[DOI:1109/EMBC.2017.8037243http://dx.doi.org/1109/EMBC.2017.8037243]
Iqbal S, Ghani M U, Saba T and Rehman A. 2018. Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN). Microscopy Research and Technique, 81(4):419-427[DOI:10.1002/jemt.22994]
Isensee F, Kickingereder P, Wick W, Bendszus M and Maier-Hein K H. 2018. Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge//Proceedings of the 3rd International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City, Canada: Springer: 287-297[DOI:10.1007/978-3-319-75238-9_25http://dx.doi.org/10.1007/978-3-319-75238-9_25]
Jiang G P, Qin W J, Zhou S J and Wang C M. 2015. State-of-the-art in medical image segmentation. Chinese Journal of Computers, 38(6):1222-1242
江贵平, 秦文健, 周寿军, 王昌淼. 2015.医学图像分割及其发展现状.计算机学报, 38(6):1222-1242[DOI:10.11897/SP.J.1016.2015.01222]
Kamnitsas K, Bai W, Ferrante E, McDonagh S, Sinclair M, Pawlowski N, Rajchl M, Lee M, Kainz B, Rueckert D and Glocker B. 2018. Ensembles of multiple models and architectures for robust brain tumour segmentation//Proceedings of the 3rd International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City, Canada: Springer: 450-462[DOI:10.1007/978-3-319-75238-9_38http://dx.doi.org/10.1007/978-3-319-75238-9_38]
Kamnitsas K, Ferrante E, Parisot S, Ledig C, Nori A V, Criminisi A, Rueckert D and Glocker B. 2016. DeepMedic for brain tumor segmentation//Proceedings of the 2nd International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Athens, Greece: Springer: 138-149[DOI:10.1007/978-3-319-55524-9_14http://dx.doi.org/10.1007/978-3-319-55524-9_14]
Kamnitsas K, Ledig C, Newcombe V F J, Simpson J P, Kane A D, Menon D K, Rueckert D and Glocker B. 2017. 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]
Kong X M, Sun G X, Wu Q, Liu J and Lin F M. 2018. Hybrid pyramid U-Net model for brain tumor segmentation//Proceedings of the 10th International Conference on Intelligent Information Processing IX. Nanning, China: Springer: 346-355[DOI:10.1007/978-3-030-00828-4_35http://dx.doi.org/10.1007/978-3-030-00828-4_35]
Krizhevsky A, Sutskever I and Hinton G E. 2012. Imagenet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, USA: Curran Associates Inc.: 1097-1105
Li P H, Xie J T, Wang Q L and Gao Z L. 2018a. Towards faster training of global covariance pooling networks by iterative matrix square root normalization//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 947-955[DOI:10.1109/CVPR.2018.00105http://dx.doi.org/10.1109/CVPR.2018.00105]
Li X, Wang W H, Hu X L and Yang J. 2019. Selective kernel networks//Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 510-519
Li Z J, Wang Y Y and Yu J H. 2018b. Brain tumor segmentation using an adversarial network//Proceedings of the 3rd International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City, Canada: Springer: 123-132[DOI:10.1007/978-3-319-75238-9_11http://dx.doi.org/10.1007/978-3-319-75238-9_11]
Lin Y and Tian J. 2002. A survey on medical image segmentation methods. Pattern Recognition and Artificial Intelligence, 15(2):192-204
林瑶, 田捷. 2002.医学图像分割方法综述.模式识别与人工智能, 15(2):192-204[DOI:10.3969/j.issn.1003-6059.2002.02.012]
Liu D, Zhang H, Zhao M M, Yu X J, Yao S W and Zhou W. 2018. Brain tumor segmention based on dilated convolution refine networks//Proceedings of the 16th IEEE International Conference on Software Engineering Research, Management and Applications. Kunming, China: IEEE: 113-120[DOI:10.1109/SERA.2018.8477213http://dx.doi.org/10.1109/SERA.2018.8477213]
Liu Y, Gadepalli K, Norouzi M, Dahl G E, Kohlberger T, Boyko A, Venugopalan S, Timofeev A, Nelson P Q, Corrado G S, Hipp J D, Peng L and Stumpe M C. 2017. Detecting cancer metastases on gigapixel pathology images[EB/OL].[2019-07-27].https://arxiv.org/pdf/1703.02442.pdfhttps://arxiv.org/pdf/1703.02442.pdf
Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE: 3431-3440[DOI:10.1109/CVPR.2015.7298965http://dx.doi.org/10.1109/CVPR.2015.7298965]
Luc P, Couprie C, Chintala S and Verbeek J. 2016. Semantic segmentation using adversarial networks[EB/OL].[2019-07-27].https://arxiv.org/pdf/1611.08408.pdfhttps://arxiv.org/pdf/1611.08408.pdf
Moeskops P, Veta M, Lafarge M W, Eppenhof K A J and Pluim J P W. 2017. Adversarial training and dilated convolutions for brain MRI segmentation//Proceedings of the 3rd International on Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Québec City, Canada: Springer: 56-64[DOI:10.1007/978-3-319-67558-9_7http://dx.doi.org/10.1007/978-3-319-67558-9_7]
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]
Radford A, Metz L and Chintala S. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks[EB/OL].[2019-07-27].https://arxiv.org/pdf/1511.06434.pdfhttps://arxiv.org/pdf/1511.06434.pdf
Randhawa R S, Modi A, Jain P and Warier P. 2016. Improving boundary classification for brain tumor segmentation and longitudinal disease progression//Proceedings of the 2nd International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Athens, Greece: Springer: 65-74[DOI:10.1007/978-3-319-55524-9_7http://dx.doi.org/10.1007/978-3-319-55524-9_7]
Ren S Q, He K M, Girshick R and Sun J. 2017. Faster R-CNN:Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6):1137-1149[DOI:10.1109/TPAMI.2016.2577031]
Rezaei M, Harmuth K, Gierke W, Kellermeier T, Fischer M, Yang H J and Meinel C. 2018. A conditional adversarial network for semantic segmentation of brain tumor//Proceedings of the 3rd International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City, Canada: Springer: 241-252[DOI:10.1007/978-3-319-75238-9_21http://dx.doi.org/10.1007/978-3-319-75238-9_21]
Rezaei M, Yang H J and Meinel C. 2019. voxel-GAN: adversarial framework for learning imbalanced brain tumor segmentation//Proceedings of the 4th International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Granada, Spain: Springer: 321-333[DOI:10.1007/978-3-030-11726-9_29http://dx.doi.org/10.1007/978-3-030-11726-9_29]
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, Germany: Springer: 234-241[DOI:10.1007/978-3-319-24574-4_28http://dx.doi.org/10.1007/978-3-319-24574-4_28]
Shaikh M, Anand G, Acharya G, Amrutkar A, Alex V and Krishnamurthi G. 2018. Brain tumor segmentation using dense fully convolutional neural network//Proceedings of the 3rd International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City, Canada: Springer: 309-319[DOI:10.1007/978-3-319-75238-9_27http://dx.doi.org/10.1007/978-3-319-75238-9_27]
Shen H C, Wang R X, Zhang J G and McKenna S. 2017a. Multi-task fully convolutional network for Brain tumour segmentation//Proceedings of the 21st Annual Conference on Medical Image Understanding and Analysis. Edinburgh, United Kingdom: Springer: 239-248[DOI:10.1007/978-3-319-60964-5_21http://dx.doi.org/10.1007/978-3-319-60964-5_21]
Shen H C, Zhang J G and Zheng W S. 2017b. Efficient symmetry-driven fully convolutional network for multimodal brain tumor segmentation//Proceedings of 2017 IEEE International Conference on Image Processing. Beijing, China: IEEE: 3864-3868[DOI:10.1109/ICIP.2017.8297006http://dx.doi.org/10.1109/ICIP.2017.8297006]
Simonyan K and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition[EB/OL].[2019-07-27].https://arxiv.org/pdf/1409.1556.pdfhttps://arxiv.org/pdf/1409.1556.pdf
Soltaninejad M, Zhang L, Lambrou T, Allinson N and Ye X J. 2017. Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network[EB/OL].[2019-07-27].https://arxiv.org/pdf/1704.08134.pdfhttps://arxiv.org/pdf/1704.08134.pdf
Tseng K L, Lin Y L, Hsu W and Huang C Y. 2017. Joint sequence learning and cross-modality convolution for 3D biomedical segmentation//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, Hawaii, USA: IEEE: 3739-3746[DOI:10.1109/CVPR.2017.398http://dx.doi.org/10.1109/CVPR.2017.398]
Urban G, Bendszus M, Hamprecht F A and Kleesiek J. 2014. Multi-modal brain tumor segmentation using deep convolutional neural networks//Proceedings of the MICCAI-BraTSs. Boston, USA: [s.n.]: 31-35
Wang G T, Li W Q, Ourselin S and Vercauteren T. 2018. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks//Proceedings of the 3rd International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City, Canada: Springer: 178-190[DOI:10.1007/978-3-319-75238-9_16http://dx.doi.org/10.1007/978-3-319-75238-9_16]
Xue Y, Xu T, Zhang H, Long L R and Huang X L. 2018. Segan:adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics, 16(3/4):383-392[DOI:10.1007/s12021-018-9377-x]
Yu H P and Wu L B. 2011. Brain tumors and brain tumor research progress in image classification. Progress in Modern Biomedicine, 11(22):4398-4400
俞海平, 邬立保. 2011.脑肿瘤及脑肿瘤图像分类的研究进展.现代生物医学进展, 11(22):4398-4400[DOI:10.13241/j.cnki.pmb.2011.22.047]
Zhao L Y and Jia K B. 2015. Deep feature learning with discrimination mechanism for brain tumor segmentation and diagnosis//Proceedings of 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing. Adelaide, Australia: IEEE: 306-309[DOI:10.1109/IIH-MSP.2015.41http://dx.doi.org/10.1109/IIH-MSP.2015.41]
Zhao X M, Wu Y H, Song G D, Li Z Y, Zhang Y Z and Fan Y. 2018a. 3D Brain tumor segmentation through integrating multiple 2D FCNNs//Proceedings of the 3rd International Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Quebec City, Canada: Springer: 191-203[DOI:10.1007/978-3-319-75238-9_17http://dx.doi.org/10.1007/978-3-319-75238-9_17]
Zhao X M, Wu Y H, Song G D, Li Z Y, Zhang Y Z and Fan Y. 2018b. 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]
Zikic D, Ioannou Y, Brown M and Criminisi A. 2014. Segmentation of brain tumor tissues with convolutional neural networks//Proceedings of the MICCAI-BRATS. Boston, USA: [s.n.]: 36-39
相关作者
相关机构
京公网安备11010802024621