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基于级联随机森林与活动轮廓的3D MR图像分割

马超 刘亚淑 骆功宁 王宽全

马超, 刘亚淑, 骆功宁, 王宽全. 基于级联随机森林与活动轮廓的3D MR图像分割. 自动化学报, 2019, 45(5): 1004-1014. doi: 10.16383/j.aas.c170520
引用本文: 马超, 刘亚淑, 骆功宁, 王宽全. 基于级联随机森林与活动轮廓的3D MR图像分割. 自动化学报, 2019, 45(5): 1004-1014. doi: 10.16383/j.aas.c170520
MA Chao, LIU Ya-Shu, LUO Gong-Ning, WANG Kuan-Quan. Combining Concatenated Random Forests and Active Contour for the 3D MR Images Segmentation. ACTA AUTOMATICA SINICA, 2019, 45(5): 1004-1014. doi: 10.16383/j.aas.c170520
Citation: MA Chao, LIU Ya-Shu, LUO Gong-Ning, WANG Kuan-Quan. Combining Concatenated Random Forests and Active Contour for the 3D MR Images Segmentation. ACTA AUTOMATICA SINICA, 2019, 45(5): 1004-1014. doi: 10.16383/j.aas.c170520

基于级联随机森林与活动轮廓的3D MR图像分割

doi: 10.16383/j.aas.c170520
基金项目: 

国家自然科学基金 61571165

详细信息
    作者简介:

    马超  东北林业大学工程技术学院讲师.哈尔滨工业大学计算机科学与技术学院博士研究生.主要研究方向为医学图像处理, 物流信息系统.E-mail:machao@nefu.edu.cn

    刘亚淑  哈尔滨工业大学计算机科学与技术学院博士研究生.主要研究方向为医学图像处理和深度学习.E-mail:yashuliu@stu.hit.edu.cn

    骆功宁  哈尔滨工业大学计算机科学与技术学院博士研究生.2014年获得哈尔滨工业大学硕士学位.主要研究方向为医学图像处理, 模式识别, 深度学习.E-mail:luogongning@hit.edu.cn

    通讯作者:

    王宽全  哈尔滨工业大学计算机科学与技术学院教授.IEEE高级会员, 中国计算机学会高级会员.主要研究方向为图像处理与模式识别, 生物计算, 生物特征识别, 虚拟现实与可视化.本文通信作者.E-mail:wangkq@hit.edu.cn

Combining Concatenated Random Forests and Active Contour for the 3D MR Images Segmentation

Funds: 

National Natural Science Foundation of China 61571165

More Information
    Author Bio:

     Lecturer at the College of Engineering and Technology, Northeast Forestry University. Ph. D. candidate at the School of Computer Science and Technology, Harbin Institute of Technology. His research interest covers medical image processing and logistics information system

    LIU Ya-Shu Ph. D. candidate at the School of Computer Science and Technology, Harbin Institute of Technology. Her research interest covers medical image processing and deep learning

     Ph. D. candidate at the Perception Computing Center of the School of Computer Science and Technology, Harbin Institute of Technology. He received his master degree at Harbin Institute of Technology in 2014. His research interest covers medical image processing, pattern recognition, and deep learning

    Corresponding author: WANG Kuan-Quan  Professor at the School of Computer Science and Technology, Harbin Institute of Technology. He is a senior member of IEEE, a senior member of China Computer Federation. His research interest covers image processing and pattern recognition, biocomputing, biometrics, virtual reality and visualization. Corresponding author of this paper
  • 摘要: 针对医学磁共振(Magnetic resonance,MR)图像三维分割中随机森林(Random forest,RF)方法难以获得具有几何约束的结果以及活动轮廓模型(Active contour model,ACM)不能自动分割发生信号混叠的组织结构的问题,提出了一种整合了级联随机森林与活动轮廓模型的磁共振图像三维分割方法.该方法首先从多模态磁共振体数据中提取图像多尺度局部鲁棒统计信息,以此驱动级联随机森林对磁共振图像进行迭代的体素分类,从而获得对组织结构的初步分割结果,进一步将此结果作为初始轮廓与形状先验,整合进一个尺度可调的活动轮廓模型中,将独立的体素分类转化为轮廓曲线演化,最终得到具有几何约束的精确分割结果.在公开数据集上的实验结果表明,本文的自动化分割方法在分割精度和鲁棒性等方面,相比其他同类方法具有较大的性能提升.
    1)  本文责任编委 张道强
  • 图  1  分割框架流程图

    Fig.  1  Flowchart of the proposed segmentation framework

    图  2  随机鲁棒统计特征学习方案二维示意图

    Fig.  2  A two-dimensional illustration of the random robust statistics features learning scheme

    图  3  级联架构下的体素分类流程图

    Fig.  3  Overview of the voxel-wise classification within the proposed concatenated scheme

    图  4  级联随机森林对多个目标图像在不同层级做出的组织概率图的估计

    Fig.  4  The tissue probability maps estimated from different levels of the concatenated random forests for several target subjects

    图  5  分割框架不同阶段的分割结果对比

    Fig.  5  Comparison of different components in the proposed segmentation framework

    图  6  低质量磁共振图像体数据三维分割结果在不同视角下的多个切片图像

    Fig.  6  Multiple slices of the 3D segmentation results for low quality volumetric MR images in different views

    图  7  级联随机森林不同参数对分割结果的影响

    Fig.  7  Impact of different parameters in the concatenated random forests

    表  1  不同分割方法在左心房数据集上的DC系数对比

    Table  1  Dice coefficients (DC) of different methods on the left atrial dataset

    数据集 Proposed1 Proposed2 ACM RF Competitive contours ${\rm{M^3AS}}$ CRF CRF-AC
    训练集 0.78$\, \pm\, $0.006 0.94$\, \pm\, $0.04 0.72$\, \pm\, $0.20 0.69$\, \pm\, $0.12 --- --- --- ---
    测试集 0.74$\, \pm\, $0.07 0.90$\, \pm\, $0.03 0.71$\, \pm\, $0.20 0.63$\, \pm\, $0.14 0.92 0.88$\, \pm\, $0.06 0.74$\, \pm\, $0.07 0.93$\, \pm\, $0.05
    下载: 导出CSV

    表  2  不同方法在BRATS15测试集上的对比

    Table  2  Comparison of different methods on the BRATS15 test set

    Methods DC PPV Sensitivity
    Complete Core Enh Complete Core Enh Complete Core Enh
    Proposed 2 0.87 0.74 0.64 0.85 0.81 0.61 0.91 0.74 0.73
    ${\rm{I_{NPUT}C_{ASCADE}CNN}}$ 0.88 0.79 0.73 --- --- --- 0.87 0.79 0.80
    CNNs 0.78 0.65 0.75 --- --- --- --- ---
    FCNNs-CRFs 0.84 0.73 0.62 0.89 0.76 0.63 0.82 0.76 0.67
    DeepMedic 0.85 0.67 0.63 0.85 0.85 0.63 0.88 0.61 0.66
    下载: 导出CSV
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  • 收稿日期:  2017-09-13
  • 录用日期:  2018-04-22
  • 刊出日期:  2019-05-20

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