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首页> 外文期刊>Biocybernetics and biomedical engineering >A deep learning model integrating SK-TPCNN and random forests for brain tumor segmentation in MRI
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A deep learning model integrating SK-TPCNN and random forests for brain tumor segmentation in MRI

机译：跨越MRI脑肿瘤细分的SK-TPCNN和随机林的深度学习模型

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摘要

The segmentation of brain tumors in magnetic resonance imaging (MRI) images plays an important role in early diagnosis, treatment planning and outcome evaluation. However, due to gliomas' significant diversity in structure, the segmentation accuracy is low. In this paper, an automatic segmentation method integrating the small kernels two-path convolutional neural network (SK-TPCNN) and random forests (RF) is proposed, the feature extraction ability of SK-TPCNN and the joint optimization capability of model are presented respectively. The SK-TPCNN structure combining the small convolutional kernels and large convolutional kernels can enhance the nonlinear mapping ability and avoid over-fitting, the multiformity of features is also increased. The learned features from SK-TPCNN are then applied to the RF classifier to implement the joint optimization. RF classifier effectively integrates redundancy features and classify each MRI image voxel into normal brain tissues and different parts of tumor. The proposed algorithm is validated and evaluated in the Brain Tumor Segmentation Challenge (Brats) 2015 challenge Training dataset and the better performance is achieved. (C) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
机译：磁共振成像（MRI）图像中脑肿瘤的分割在早期诊断，治疗计划和结果评估中起着重要作用。但是，由于胶质组织结构的显着多样性，分割精度低。本文提出了一种自动分割方法，分别介绍了整合小核两径卷积神经网络（SK-TPCNN）和随机林（RF）的自动分割方法，分别提出了SK-TPCNN的特征提取能力和模型的联合优化能力。组合小型卷积核和大型卷积核的SK-TPCNN结构可以增强非线性映射能力，避免过度拟合，多种特征的特性也增加。然后将来自SK-TPCNN的学习功能应用于RF分类器以实现联合优化。 RF分类器有效地集成了冗余特征，并将每个MRI图像体素分类为正常的脑组织和肿瘤的不同部分。所提出的算法在脑肿瘤分割挑战（Brats）2015挑战训练数据集中进行了验证和评估，实现了更好的性能。 （c）2019年纳雷斯州博士科学学院生物医学研究所。 elsevier b.v出版。保留所有权利。

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    Yang Tiejun; Song Jikun; Li Lei;

  • 作者单位

    Henan Univ Technol Coll Informat Sci &

    Technol 100 LianHua St Zhengzhou Henan Peoples R China;

    Henan Univ Technol Coll Informat Sci &

    Technol 100 LianHua St Zhengzhou Henan Peoples R China;

    Henan Univ Technol Coll Informat Sci &

    Technol 100 LianHua St Zhengzhou Henan Peoples R China;

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  • 正文语种 eng
  • 中图分类 医用一般科学;
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