基于RV-FCN的CT肝脏影像自动分割算法

doi:10.19678/j.issn.1000-3428.0052132

计算机工程 ›› 2019, Vol. 45 ›› Issue (7): 258-263. doi: 10.19678/j.issn.1000-3428.0052132

基于RV-FCN的CT肝脏影像自动分割算法

张杰妹, 杨词慧

南昌航空大学信息工程学院, 南昌 330063

收稿日期:2018-07-16 修回日期:2018-08-27 出版日期:2019-07-15 发布日期:2019-07-23
作者简介:张杰妹(1992-),女,硕士研究生,主研方向为图像处理、深度学习;杨词慧(通信作者),副教授、博士。
基金资助:
国家自然科学基金青年项目（61402218）；江西省教育厅科学基金（GJJ180516）；江西省图像处理与模式识别重点实验室开放基金（TX201304002）。

Automatic Segmentation Algorithm of CT Liver Image Based on RV-FCN

ZHANG Jiemei, YANG Cihui

School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China

Received:2018-07-16 Revised:2018-08-27 Online:2019-07-15 Published:2019-07-23

摘要/Abstract

摘要： 由于肝脏的大小、形状因人而异，且CT影像中肝脏与其毗邻器官的灰度对比值较低，难以精准地判断肝脏影像的边界信息。为此，提出一种基于全卷积神经网络（FCN）的改进算法，在FCN的基础上引入残差和VGG-16网络，得到肝脏影像的初始分割结果。引入批归一化和PReLU激活函数，提高网络的泛化能力和收敛速度。采用条件随机场方法，进一步优化分割结果，提高分割准确率。通过VTK和ITK系统对二维肝脏影像进行三维重建。在3DIRCADb数据集上的实验结果验证了该算法的有效性和高效性。

关键词: 肝脏分割, 全卷积神经网络, 残差网络, 批归一化, 条件随机场

Abstract: Since the size and shape of the liver vary from person to person,and the grayscale contrast value of the liver and its adjacent organs in the CT image is low,it is difficult to accurately determine the boundary information of the liver image.Aiming at these problems,this paper proposes an improved algorithm based on Fully Convolutional neural Network (FCN).Based on the FCN,the residual and VGG-16 networks are introduced to obtain the initial segmentation result of the liver.The Batch Normalization (BN) and PReLU activation functions are introduced to improve the generalization ability and convergence speed of the network.Conditional Random Field (CRF) method is used to further optimize the segmentation result and improve the segmentation accuracy.The 2-dimensional liver segmentation result is reconstructed into a 3-dimensional structure by the system of VTK and ITK.The effectiveness and efficiency of the algorithm are verified by the experimental results on the 3DIRCADb date set.

Key words: liver segmentation, Fully Convolutional neural Network(FCN), residual network, Batch Normalization(BN), Conditional Random Field(CRF)

中图分类号:

TP391

张杰妹, 杨词慧. 基于RV-FCN的CT肝脏影像自动分割算法[J]. 计算机工程, 2019, 45(7): 258-263.

ZHANG Jiemei, YANG Cihui. Automatic Segmentation Algorithm of CT Liver Image Based on RV-FCN[J]. Computer Engineering, 2019, 45(7): 258-263.

https://www.ecice06.com/CN/Y2019/V45/I7/258

参考文献

[1] 马树志.基于深度学习的肝脏CT影像分割方法的研究与应用[D].长春:吉林大学,2017.
[2] SAITO K,LU Huiming,TAN J K,et al.Automatic liver segmentation from multiphase CT images by using level set method[C]//Proceedings of International Conference on Control,Automation and Systems.Washington D.C.,USA:DEStech Publications,2017:1590-1592.
[3] KRISHNAN K R,RADHAKRISHNAN S.Hybrid approach to classification of focal and diffused liver disorders using ultrasound images with wavelets and texture features[J].IET Image Processing,2017,11(7):530-538.
[4] KUO Chaolun,CHENG Shyichyi,LIN Chihlang,et al.Texture-based treatment prediction by automatic liver tumor segmentation on computed tomography[C]//Proceedings of International Conference on Computer,Information and Telecommunication Systems.Washington D.C.,USA:IEEE Press,2017:128-132.
[5] LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.
[6] SUN Bin,MA Chunhui,JIN Xinyu,et al.Sparse segmentation algorithm of liver in CT images[C]//Proceedings of International Symposium on Computational Intelligence and Design.Washington D.C.,USA:IEEE Press,2017:457-460.
[7] BEN-COHEN A,KLANG E,KERPEL A,et al.Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations[J].Neurocomputing,2018,275:1585-1594.
[8] 郭树旭,马树志,李晶,等.基于全卷积神经网络的肝脏CT影像分割研究[J].计算机工程与应用,2017,53(18):126-131.
[9] ZHANG Yao,HE Zhiqiang,ZHONG Cheng,et al.Fully convolutional neural network with post-processing methods for automatic liver segmentation from CT[C]//Proceedings of Chinese Automation Congress.Washington D.C.,USA:IEEE Press,2017:3864-3869.
[10] RONNEBERGER O,FISCHER P,BROX T.U-net:convolutional networks for biomedical image segmentation[EB/OL].[2018-07-01].https://arxiv.org/pdf/1505.04597.pdf.
[11] CHRIST P F,ELSHAER M E A,ETTLINGER F,et al.Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention.Berlin,Germany:Springer,2016:415-423.
[12] 高静波,吴成茂,田小平,等.基于清晰度的彩色图像量子增强算法[J].西安邮电大学学报,2011,16(2):4-8.
[13] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Computer Society,2015:3431-3440.
[14] HE Kaiming,ZHANG Xianyu,REN Shaoqing,et al.Delving deep into rectifiers:surpassing human-level performance on ImageNet classification[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:1026-1034.
[15] ZHANG Peng,LIU Jie,CHEN Chen,et al.The algorithm study for using the back propagation neural network in CT image segmentation[C]//Proceedings of International Conference on Innovative Optical Health Science.Washington D.C.,USA:International Society for Optics and Photonics,2017:1-4.
[16] 张明,吕晓琪,吴凉,等.基于深度残差学习的乘性噪声去噪方法[J].激光与光电子学进展,2018,55(3):41-47.
[17] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[EB/OL].[2018-07-01].https://arxiv.org/pdf/1409.1556.pdf.
[18] SUN Changjian,GUO Shuxu,ZHANG Huimao,et al.Liver lesion segmentation in CT images with MK-FCN[C]//Proceedings of Advanced Information Technology,Electronic and Automation Control Conference.Washington D.C.,USA:IEEE Press,2017:1794-1798.
[19] NIE D,WANG Li,ADELI E,et al.3D fully convolutional networks for multimodal isointense infant brain image segmentation[J].IEEE Transactions on Cybernetics,2018,99:1-14.
[20] 李泽宇,陈一民,赵艳,等.拟合正态分布曲线的肺野图像分割与三维重建[J].计算机工程与设计,2017,38(5):1277-1281.

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