基于级联卷积神经网络的前列腺磁共振图像分类

doi:10.11938/cjmr20192717

波谱学杂志 ›› 2020, Vol. 37 ›› Issue (2): 152-161.doi: 10.11938/cjmr20192717

基于级联卷积神经网络的前列腺磁共振图像分类

刘可文¹, 刘紫龙^1,2, 汪香玉³, 陈黎², 李钊², 吴光耀⁴, 刘朝阳²

1. 武汉理工大学信息工程学院, 湖北武汉 430070;
2. 波谱与原子分子物理国家重点实验室, 武汉磁共振中心(中国科学院精密测量科学与技术创新研究院), 湖北武汉 430071;
3. 深圳市第二人民医院医学影像科, 广东深圳 518037;
4. 深圳大学总医院医学影像科, 广东深圳 518055

收稿日期:2019-02-28 出版日期:2020-06-05 发布日期:2019-05-30
通讯作者: 刘可文,Tel:18986086863,E-mail:liukewen@whut.edu.cn. E-mail:liukewen@whut.edu.cn
基金资助:
国家重点研发计划资助项目（2018YFC0115100）.

Prostate Cancer Diagnosis Based on Cascaded Convolutional Neural Networks

LIU Ke-wen¹, LIU Zi-long^1,2, WANG Xiang-yu³, CHEN Li², LI Zhao², WU Guang-yao⁴, LIU Chao-yang²

1. School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China;
2. State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan(Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences), Wuhan 430071, China;
3. Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen 518037, China;
4. Department of Radiology, Shenzhen University General Hospital, Shenzhen 518055, China

Received:2019-02-28 Online:2020-06-05 Published:2019-05-30

摘要/Abstract

摘要： 针对深度学习训练成本高，以及基于磁共振图像的前列腺癌临床诊断需要大量医学常识且极为耗时的问题，本文提出了一种基于级联卷积神经网络（Convolutional Neural Network，CNN）和磁共振图像的前列腺癌（Prostate Cancer，PCa）自动分类诊断方法，该网络以Faster-RCNN作为前网络，对前列腺区域进行提取分割，用于排除前列腺附近组织器官的干扰；以基于ResNet改进的网络结构CNN40_bottleneck作为后网络，用于对前列腺区域病变进行分类.后网络由瓶颈结构串联组成，其中使用批量标准化（Batch Normalization，BN）、全局平均池化（Global Average Pooling，GAP）进行优化.实验结果证明，本文方法对前列腺癌诊断结果较好，而且缩减了训练时间和参数量，有效降低了训练成本.

关键词: 磁共振成像(MRI), 级联卷积神经网络(Cascaded CNN), 前列腺癌(PCa), 分类

Abstract: Interpreting magnetic resonance imaging (MRI) data by radiologists is time consuming and demands special expertise. Diagnosis of prostate cancer (PCa) with deep learning can also be time consuming and data storage consuming. This work presents an automated method for PCa detection based on cascaded convolutional neural network (CNN), including pre-network and post-network. The pre-network is based on a Faster-RCNN and trained with prostate images in order to separate the prostate from nearby tissues; the ResNet-based post-network is for PCa diagnosis, which is connected by bottlenecks and improved by applying batch normalization (BN) and global average pooling (GAP). The experimental results demonstrated that the cascaded CNN proposed had a good classification results on the in-house datasets, with less training time and computation resources.

Key words: magnetic resonance imaging (MRI), cascaded convolutional neural network (Cascaded CNN), prostate cancer (PCa), classification

中图分类号:

O482.53

刘可文, 刘紫龙, 汪香玉, 陈黎, 李钊, 吴光耀, 刘朝阳. 基于级联卷积神经网络的前列腺磁共振图像分类[J]. 波谱学杂志, 2020, 37(2): 152-161.

LIU Ke-wen, LIU Zi-long, WANG Xiang-yu, CHEN Li, LI Zhao, WU Guang-yao, LIU Chao-yang. Prostate Cancer Diagnosis Based on Cascaded Convolutional Neural Networks[J]. Chinese Journal of Magnetic Resonance, 2020, 37(2): 152-161.

参考文献

[1] SIEGEL R L, MILLER K D, DVM A J. Cancer statistics, 2018[J]. Ca:A Cancer Journal for Clinicians, 2018, 68(1):11.
[2] HAN S J, ZHANG S W, CHEN W Q, et al. Analysis of the status and trends of prostate cancer incidence in China[J]. Chinese Clinical Oncology, 2013, 18(4):330-334. 韩苏军, 张思维, 陈万青, 等. 中国前列腺癌发病现状和流行趋势分析[J]. 临床肿瘤学杂志, 2013, 18(4):330-334.
[3] YANG J Y, YANG M Z, WEI W. Epidemiological study on the occurrence and development of prostate cancer[J]. J Clin Urology, 2017, 9:74-78. 杨进益, 杨明州, 魏伟. 前列腺癌发生发展的流行病学研究进展[J]. 临床泌尿外科杂志, 2017, 9:74-78.
[4] MADDAMS J, UTLEY M, MØLLER H. Projections of cancer prevalence in the United Kingdom, 2010-2040[J]. Brit J Cancer, 2012, 107(7):1195-1202.
[5] PENG Y, JIANG Y, YANG C, et al. Quantitative analysis of multiparametric prostate MR images:differentiation between prostate cancer and normal tissue and correlation with Gleason score——a computer-aided diagnosis development study[J]. Radiology, 2013, 267(3):787-796.
[6] ROOIJ M D, HAMOEN E H J, FÜTTERER J J, et al. Accuracy of multiparametric MRI for prostate cancer detection:A meta-analysis[J]. AJR Am J Roentgenol, 2014, 202(2):343-351.
[7] FEHR D, VEERARAGHAVAN H, WIBMER A, et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images[J]. Proc Natl Acad Sci U S A, 2015, 112(46):6265-73.
[8] PENG Y, JIANG Y, YANG C, et al. Quantitative analysis of multiparametric prostate mr images:differentiation between prostate cancer and normal tissue and correlation with gleason score—a computer-aided diagnosis development study[J]. Radiology, 2013, 267(3):787-796.
[9] HASEGAWA A, LO S C B, FREEDMAN M T, et al. Convolution neural-network-based detection of lung structures[J]. Proceedings of SPIE-The International Society for Optical Engineering, 1994:654-662.
[10] LO S B, LOU S A, LIN J S, et al. Artificial convolution neural network techniques and applications for lung nodule detection[J]. IEEE Trans Med Imaging, 1995, 14(4):711-718.
[11] SAHINER B, CHAN H P, PETRICK N, et al. Classification of mass and normal breast tissue:a convolution neural network classifier with spatial domain and texture images[J]. IEEE Trans Med Imaging, 1996, 15(5):598-610.
[12] YANG X, LIU C Y, WANG Z W, et al. Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI[J]. Med Image Anal, 2017, 42:212-227.
[13] WANG Z W, LIU C Y, CHENG D P, et al. Automated detection of clinically significant prostate cancer in mp-MRI images based on an end-to-end deep neural network[J]. IEEE Trans Med Imaging, 2018, 37(5):1127-1139.
[14] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014. arXiv:1409.1556.
[15] REN S, HE K, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[C]//International Conference on Neural Information Processing Systems. 2015.
[16] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2016:770-778.
[17] LIN M, CHEN Q, YAN S C. Network in network[J]. Computer Science, 2013:196-204.
[18] IOFFE S, SZEGEDY C. Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//International Conference on International Conference on Machine Learning. JMLR.org, 2015.
[19] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015:1-9.
[20] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
[21] SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, Inception-ResNet and the impact of residual connections on learning[C]//Thirty-First AAAI Conference on Artificial Intelligence. 2017:711-718.
[22] GEERT L, OSCAR D, JELLE B, et al. Prostatex challenge data. The cancer imaging archive[OL]. https://doi.org/10.7937/K9TCIA.2017.MURS5CL.
[23] SCHAEFER S, MCPHAIL T, WARREN J. Image deformation using moving least squares[C]//ACM Siggraph. 2006:711-718.

基于级联卷积神经网络的前列腺磁共振图像分类

Prostate Cancer Diagnosis Based on Cascaded Convolutional Neural Networks

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	孙京文, 闫士举, 韩勇森, 宋成利. 基于脑部磁共振图像三维局部模式变换特征提取进行阿尔茨海默病病程预测分类[J]. 波谱学杂志, 2019, 36(3): 268-277.
[2]	魏国境, 易佩伟, 陶泉, 冯衍秋. CEST成像不同量化方式在急性帕金森氏病小鼠模型研究中的应用比较[J]. 波谱学杂志, 2019, 36(2): 195-207.
[3]	刘颖, 宋明辉, 王坤, 章浩伟. 基于全可编程SoC和LabVIEW的磁共振接收系统设计[J]. 波谱学杂志, 2018, 35(4): 475-485.
[4]	汪红志, 赵地, 杨丽琴, 夏天, 周皛月, 苗志英. 基于AI+MRI的影像诊断的样本增广与批量标注方法[J]. 波谱学杂志, 2018, 35(4): 447-456.
[5]	陈海燕, 赵世龙, 李晓南, 刘国强, 胡丽丽, 刘弢. 低场永磁体磁共振射频场映像[J]. 波谱学杂志, 2018, 35(4): 498-504.
[6]	陶泉, 易佩伟, 魏国境, 冯衍秋. 基于CEST机制的pH成像方法、原理和应用[J]. 波谱学杂志, 2018, 35(4): 505-519.
[7]	翟国强, 张苗, 薄斌仕, 王乙, 范明霞, 李建奇. 基于复值和模值的肝脏磁共振水脂分离组合算法[J]. 波谱学杂志, 2018, 35(4): 417-426.
[8]	黄朝晖, 张志, 陈黎, 陈俊飞, 张震, 陈方, 刘朝阳. MRI梯度预加重模块的分时复用设计[J]. 波谱学杂志, 2018, 35(4): 465-474.
[9]	张波, 谢海滨, 严序, 李文静, 杨光. 旋转不变的非局域均值算法在磁共振图像去噪中的应用[J]. 波谱学杂志, 2018, 35(2): 162-169.
[10]	孙炜航, 孙钰, 汪丰, 王超洪, 杨涛. 变延迟进动定制激发(DANTE)序列在工程中的优化设计[J]. 波谱学杂志, 2018, 35(2): 150-161.
[11]	崔妍, 肖亮. 磁共振成像谱仪B₀信号的高精度发生与测试[J]. 波谱学杂志, 2018, 35(2): 170-177.
[12]	马芸, 郭虹, 王秋实, 张伟国, 吴昊. 基于影像的形态学特征与胶质母细胞瘤特征分子表达的相关性研究[J]. 波谱学杂志, 2018, 35(1): 22-30.
[13]	黄丽洁, 宋阳, 赵献策, 谢海滨, 吴东梅, 杨光. 一种结合并行成像和压缩感知的快速磁共振成像新方法[J]. 波谱学杂志, 2018, 35(1): 31-39.
[14]	罗庆, 张成秀, 李文静, 郑慧, 谢海滨, 杨光. 一种新的磁共振图像处理流水线的设计与实现[J]. 波谱学杂志, 2018, 35(1): 40-51.
[15]	宋瑞, 何砚发, 张波. 一种永磁磁共振中磁体涡流场的测量方法[J]. 波谱学杂志, 2018, 35(1): 52-59.