多分类CNN的胶质母细胞瘤多模态MR图像分割
Glioblastoma Multiforme Multi-modal MR Images Segmentation Using Multi-class CNN
查看参考文献23篇
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文摘
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为提高胶质母细胞瘤( GBM)多模态磁共振( MR)图像中各肿瘤子区域分割的准确性,提出一种多分类卷积神经网络( CNN)的GBM多模态MR图像自动分割算法.首先在98%缩尾处理和配准GBM多模态MR图像后,利用N4ITK法校正偏移场;其次构建一个主要由4个卷积层、2个池化层和2个全连接层组成的多分类CNN模型,训练后预分割GBM多模态MR图像,将体素分为5类不同的标签;最后移除所有小于200体素的假阳性区域,中值滤波后获得最终分割结果.以Dice相似性系数DSC、阳性预测值PPV和平均Hausdorff距离AHD为评价指标,利用所提出的算法对F-C-GBM数据集中整个肿瘤组织进行分割,获得的DSC、PPV、AHD分别为0.889 ± 0.087、0.859 ± 0.127和1.923.结果表明,该算法能有效提高GBM多模态MR图像分割的性能,可望有临床应用前景. |
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其他语种文摘
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To improve the accuracy of segmenting the tumor sub-regions in glioblastoma multiforme ( GBM) multimodal magnetic resonance ( MR) images,a GBM multi-modal MR images automatic segmentation algorithm is proposed by using multi-class convolution neural network ( CNN). Firstly, after 98% winsorization and registration for the GBM multimodal MR images, the bias field was corrected by using the N4ITK method. Secondly,a multi-class CNN model mainly consisting of four convolutional layers, two pooling layers and two fully connected layers was constructed; the GBM multi-modal MR images were pre-segmented after training, and voxels were classified into five different labels. Finally, all false positive regions smaller than 200 voxels were removed,and the final segmentation results were obtained by median filtering. The Dice similarity coefficient DSC,positive predictive value PPV and average Hausdorff distance AHD were adopted as the evaluation index, and the DSC,PPV as well as AHD were 0.889 ± 0.087, 0.859 ± 0.127 and 1.923 for segmenting the entire tumor tissues in F-C-GBM dataset by the proposed algorithm, respectively. Results indicate that the proposed method can effectively improve the performance in the segmentation of the GBM multi-modal MR images and may be expected to have clinical application prospects. |
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来源
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电子学报
,2019,47(8):1738-1747 【核心库】
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DOI
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10.3969/j.issn.0372-2112.2019.08.018
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关键词
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胶质母细胞瘤
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多模态磁共振图像
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自动分割
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多分类卷积神经网络
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图像块
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地址
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浙江中医药大学医学技术学院, 浙江, 杭州, 310053
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浙江中医药大学第一临床医学院, 浙江, 杭州, 310053
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金
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浙江省自然科学基金
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文献收藏号
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CSCD:6668615
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参考文献 共
23
共2页
|
|
1.
刘瑞.
基于多模态MRI图像的脑肿瘤分割方法,2017
|
CSCD被引
1
次
|
|
|
|
|
2.
Akkus Z. Deep learning for brain MRI segmentation: state of the art and future directions.
Journal of Digital Imaging,2017,30(4):449-459
|
CSCD被引
10
次
|
|
|
|
|
3.
Stijn B. Machine learning based brain tumour segmentation on limited data using local texture and abnormality.
Computers in Biology and Medicine,2018,98(1):39-47
|
CSCD被引
2
次
|
|
|
|
|
4.
Kaur T. A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation.
Australasian Physical & Engineering Sciences in Medicine,2018,41(1):41-58
|
CSCD被引
2
次
|
|
|
|
|
5.
Vishnuvarthanan G. Tumor detection in T1,T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed-based region growing algorithm.
International Journal of Imaging Systems and Technology,2017,27(1):33-45
|
CSCD被引
2
次
|
|
|
|
|
6.
Kavitha A R. Brain tumour segmentation from MRI image using genetic algorithm with fuzzy initialisation and seeded modified region growing ( GFSMRG) method.
Imaging Science Journal,2016,64(5):285-297
|
CSCD被引
1
次
|
|
|
|
|
7.
Sasikanth S. Glioma tumor detection in brain MRI image using ANFIS-based normalized graph cut approach.
International Journal of Imaging Systems and Technology,2018,28(1):64-71
|
CSCD被引
1
次
|
|
|
|
|
8.
Varuna S N. Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network.
Brain Informatics,2018,5(1):23-30
|
CSCD被引
4
次
|
|
|
|
|
9.
Essadike A. Brain tumor segmentation with Vander Lugt correlator based active contour.
Computer Methods and Programs in Biomedicine,2018,160(9):103-117
|
CSCD被引
1
次
|
|
|
|
|
10.
Lok K H. Fast and robust brain tumor segmentation using level set method with multiple image information.
Journal of X-Ray Science and Technology,2017,25(2):301-312
|
CSCD被引
1
次
|
|
|
|
|
11.
Karuppathal R. An automotive approach for brain tumor segmentation based on Gaussian distribution and level set method.
Current Medical Imaging Reviews,2014,10(4):290-296
|
CSCD被引
1
次
|
|
|
|
|
12.
Pope W B. MR imaging correlates of survival in patients with high-grade gliomas.
American Journal of Neuroradiology,2005,26(10):2466-2474
|
CSCD被引
13
次
|
|
|
|
|
13.
柯圣财. 基于卷积神经网络和监督核哈希的图像检索方法.
电子学报,2017,45(1):158-163
|
CSCD被引
1
次
|
|
|
|
|
14.
Jayasuriya S A. Symmetry plane detection in brain image analysis: a survey.
Current Medical Imaging Reviews,2013,9(3):230-247
|
CSCD被引
1
次
|
|
|
|
|
15.
Tustison N J. N4ITK: improved N3 bias correction.
IEEE Transactions on Medical Imaging,2010,29(6):1310-1320
|
CSCD被引
42
次
|
|
|
|
|
16.
Pereira S. Brain tumor segmentation using convolutional neural networks in MRI images.
IEEE Transactions on Medical Imaging,2016,30(5):1240-1251
|
CSCD被引
66
次
|
|
|
|
|
17.
Havaei M. Brain tumor segmentation with deep neural networks.
Medical Image Analysis,2017,35:18-31
|
CSCD被引
80
次
|
|
|
|
|
18.
Zikic D. Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR.
International Conference on Medical Image Computing and Computer-Assisted Intervention,2012:369-376
|
CSCD被引
1
次
|
|
|
|
|
19.
Meier R. Appearanceand context-sensitive features for brain tumor segmentation.
International Conference on Medical Image Computing and Computer-Assisted Intervention,2015:48-51
|
CSCD被引
1
次
|
|
|
|
|
20.
Reza S. Multi-fractal texture features for brain tumor and edema segmentation.
International Society for Optics and Photonics,2014:903503-903503
|
CSCD被引
1
次
|
|
|
|
|
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