基于多模态3D-CNNs特征提取的MRI脑肿瘤分割方法
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南方医科大学生物医学工程学院,南方医科大学生物医学工程学院,南方医科大学生物医学工程学院

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TP391.41

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Multimodal 3D Convolutional Neural Networks Features for Brain Tumor Segmentation
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school of biomedical engineering,southern medical university

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    针对目前MRI脑肿瘤分割中的无监督特征提取方法无法适应脑肿瘤图像的差异性,提出一种基于多模态3D卷积神经网络(CNNs)特征提取的MRI脑肿瘤分割方法。将2D的多模态MRI图像组合成3D原始特征,通过3D-CNNs提取特征,更有利于提取各模态之间的差异信息,去除各模态之间的冗余干扰信息,同时缩小原始特征邻域大小,以适应同一病人不同图像层肿瘤大小的差异变化,进一步提高MRI脑肿瘤的分割精度。实验结果证明,本文方法能适应不同病人各模态之间的差异性和多变性,以提高脑肿瘤的分割精度。

    Abstract:

    Because the feature extraction in MRI brain tumor segmentation can not adapt to the arbitrary and differential of the brain images, this paper proposed a novel segmentation method based on multimodal 3D convolutional neural networks (CNNs) features. As a supervised features extraction method, CNNs can extract the texture, shape and structure characteristics automatically according to the characteristics of the image itself in the training process. By combining the 2D multimodal MR images into 3D primitive characteristics and utilizing 3D-CNNs to extract features, it is more conducive to extract the different information between different modal, remove the interference information among the different modal, shrinkthe neighborhood size of the original characteristics, and adapt to different tumor size change image layer on the same and further improve the accuracy of MRI brain tumor segmentation. The experimental results showed that compared with the current unsupervised feature extraction method and 2D-CNNs, this method can adapt to the diversity and dynamics of different patients and modals, and improve the segmentation accuracy of brain tumors.

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引用本文

罗蔓,黄靖,杨丰. 基于多模态3D-CNNs特征提取的MRI脑肿瘤分割方法[J]. 科学技术与工程, 2014, 14(31): .
LUO Man,,YANG Feng. Multimodal 3D Convolutional Neural Networks Features for Brain Tumor Segmentation[J]. Science Technology and Engineering,2014,14(31).

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历史
  • 收稿日期:2014-06-05
  • 最后修改日期:2014-10-17
  • 录用日期:2014-07-17
  • 在线发布日期: 2014-11-06
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