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浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (1): 203-212    DOI: 10.3785/j.issn.1008-973X.2021.01.024
生物医学工程     
SE-Mask-RCNN：多参数MRI前列腺癌分割方法
黄毅鹏1,2(),胡冀苏1,2,钱旭升1,2,周志勇2,赵文露3,马麒3,沈钧康3,戴亚康2,*()
1. 中国科学技术大学 生物医学工程学院（苏州），江苏 苏州 215163
2. 中国科学院 苏州生物医学工程技术研究所，江苏 苏州 215163
3. 苏州大学附属第二医院，江苏 苏州 215000
SE-Mask-RCNN: segmentation method for prostate cancer on multi-parametric MRI
Yi-peng HUANG1,2(),Ji-su HU1,2,Xu-sheng QIAN1,2,Zhi-yong ZHOU2,Wen-lu ZHAO3,Qi MA3,Jun-kang SHEN3,Ya-kang DAI2,*()
1. School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou 215163, China
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
3. The Second Affiliated Hospital of Suzhou University, Suzhou 215000, China
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摘要：

为了从多参数磁共振(mp-MRI)的前列腺区域中自动提取前列腺癌病灶区域，提出新的深度卷积神经网络模型SE-Mask-RCNN. 在特征图上搜索定位包含病灶的候选区域，基于候选区域实现病灶的精细分割.为了利用mp-MRI中的互补信息，通过2个并行卷积网络分别提取表观扩散系数(ADC)和T2加权(T2W)图像的特征图后进行融合，使用挤压与激励块自动提升融合特征图中的有效特征并抑制无效特征.在收集得到的140例数据上进行实验.结果表明，使用SE-Mask-RCNN得到前列腺癌病灶分割Dice系数为0.654，敏感度为0.695，特异度为0.970，阳性预测值为0.685.与U-net、V-net、Resnet50-U-net和Mask-RCNN等模型相比，SE-Mask-RCNN能够有效提升mp-MRI中前列腺癌病灶区域的分割精度.
关键词： 前列腺癌;  深度学习;  挤压与激励块(SE-block);  Mask-RCNN;  多参数磁共振成像(mp-MRI)    
Abstract:

A new deep convolutional neural network model called SE-Mask-RCNN was proposed to automatically segment prostate cancer regions in multi-parametric magnetic resonance imaging (mp-MRI) images. The candidate regions were extracted from the feature maps, and the possible lesions were segmented within the candidate regions. The feature maps were obtained from apparent diffusion coefficient (ADC) maps and T2-weighted (T2W) images through two parallel convolution networks, which were fused to fully use complementary information. The squeeze-and-excitation block was employed to automatically boost effective features and suppress the useless features. The experiments were conducted on a dataset containing 140 patients. The experimental results showed that the proposed model achieved a Dice coefficient of 0.654, a sensitivity of 0.695, a specificity of 0.970, and a positive predictive value of 0.685. The proposed SE-Mask-RCNN can effectively improve the segmentation accuracy of prostate cancer lesions in mp-MRI images compared with U-net, V-net, Resnet50-U-net and Mask-RCNN.
Key words: prostate cancer    deep learning    squeeze-and-excitation block (SE-block)    Mask-RCNN    multi-parametric magnetic resonance imaging (mp-MRI)
收稿日期: 2020-01-02 出版日期: 2021-01-27
CLC:  R 318  
基金资助: 国家重点研发计划资助项目（2018YFA0703101）；江苏省重点研发计划资助项目（BE2017663）；苏州市科技计划资助项目（SS2019012，SS201855，SZS201818，SYS2018010，SS201854）；丽水市重点研发计划资助项目（2019ZDYF17）
通讯作者: 戴亚康     E-mail: huang96@mail.ustc.edu.cn;daiyk@sibet.ac.cn
作者简介: 黄毅鹏（1996—），男，硕士生，从事医学影像分析的研究. orcid.org/0000-0003-1661-1853. E-mail： huang96@mail.ustc.edu.cn
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黄毅鹏
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马麒
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引用本文:

黄毅鹏,胡冀苏,钱旭升,周志勇,赵文露,马麒,沈钧康,戴亚康. SE-Mask-RCNN：多参数MRI前列腺癌分割方法[J]. 浙江大学学报(工学版), 2021, 55(1): 203-212.

Yi-peng HUANG,Ji-su HU,Xu-sheng QIAN,Zhi-yong ZHOU,Wen-lu ZHAO,Qi MA,Jun-kang SHEN,Ya-kang DAI. SE-Mask-RCNN: segmentation method for prostate cancer on multi-parametric MRI. Journal of ZheJiang University (Engineering Science), 2021, 55(1): 203-212.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.01.024    或    http://www.zjujournals.com/eng/CN/Y2021/V55/I1/203

图 1  Mask-RCNN网络结构
图 2  SE-block结构图
图 3  SE-Mask-RCNN模型结构图
图 4  SE-block改进的mp-MRI信息融合方式
图 5  数据预处理示例
网络模型 MRI类型 DSC 敏感度 特异度 PPV
U-net ADC 0.539 0.639 0.970 0.324
V-net ADC 0.525 0.707 0.945 0.472
Resnet50-U-net ADC 0.525 0.720 0.956 0.462
Mask-RCNN ADC 0.592 0.582 0.979 0.680
U-net T2W 0.370 0.651 0.886 0.283
V-net T2W 0.343 0.580 0.896 0.282
Resnet50-U-net T2W 0.349 0.624 0.902 0.276
Mask-RCNN T2W 0.403 0.395 0.962 0.473
U-net ADC+T2W 0.562 0.765 0.955 0.480
V-net ADC+T2W 0.553 0.763 0.949 0.476
Resnet50-U-net ADC+T2W 0.561 0.683 0.972 0.532
Mask-RCNN ADC+T2W 0.612 0.653 0.968 0.650
表 1  前列腺癌病灶分割不同网络模型结果的定量比较
图 6  不同网络模型mp-MRI前列腺癌病灶分割结果示例
图 7  4种mp-MRI信息融合方式
融合方式 卷积网络 DSC 敏感度 特异度 PPV
输入图像串联（Mask-RCNN） Resnet50 0.612 0.653 0.968 0.650
特征图相加 Resnet50 0.611 0.667 0.966 0.643
特征图串联 Resnet50 0.609 0.645 0.968 0.659
SE-特征图串联 Resnet50 0.630 0.676 0.970 0.663
输入图像串联 SE-Resnet 0.637 0.660 0.970 0.685
特征图相加 SE-Resnet 0.638 0.684 0.970 0.658
特征图串联 SE-Resnet 0.640 0.697 0.967 0.652
SE-特征图串联（SE-Mask-RCNN） SE-Resnet 0.654 0.695 0.970 0.685
表 2  4种mp-MRI信息融合方式对比
图 8  候选区域和热力图对比
融合方式 ADC SE-Resnet是否去掉SE-block T2W SE-Resnet是否去掉SE-block DSC 敏感度 特异度 PPV
SE-特征图串联 否 否 0.654 0.695 0.970 0.685
SE-特征图串联 否 是 0.633 0.722 0.960 0.634
SE-特征图串联 是 否 0.633 0.726 0.960 0.630
SE-特征图串联 是 是 0.621 0.641 0.975 0.670
表 3  卷积网络SE-block去除方式性能比较
模型 运算时间/ms
U-net 78
V-net 84
Resnet50-U-net 85
Mask-RCNN 131
SE-Mask-RCNN 144
表 4  不同模型平均处理图像时间
1 CHEN W, ZHENG R, BAADE P D, et al Cancer statistics in China, 2015[J]. CA: A Cancer Journal for Clinicians, 2016, 66 (2): 115- 132
doi: 10.3322/caac.21338
2 CHEN W, ZHENG R, ZENG H, et al Annual report on status of cancer in China, 2011[J]. Chinese Journal of Cancer Research, 2015, 27 (1): 2
3 陈金东 中国各类癌症的发病率和死亡率现状及发展趋势[J]. 遵义医学院学报, 2018, 41 (6): 653- 662
CHEN Jin-dong Trends of cancer incidence and mortality in China[J]. Journal of Zunyi Medical University, 2018, 41 (6): 653- 662
doi: 10.3969/j.issn.1000-2715.2018.06.001
4 王宁, 刘硕, 杨雷, 等 2018全球癌症统计报告解读[J]. 肿瘤综合治疗电子杂志, 2019, 5 (1): 87- 97
WANG Ning, LIU Shuo, YANG Lei, et al Interpretation on the report of global cancer statistics 2018[J]. Journal of Multidisciplinary Cancer Management (Electronic Version), 2019, 5 (1): 87- 97
5 SIEGEL R L, MILLER K D, JEMAL A Cancer statistics, 2019[J]. CA: A Cancer Journal for Clinicians, 2019, 69 (1): 7- 34
doi: 10.3322/caac.21551
6 PANEBIANCO V, BARCHETTI F, SCIARRA A, et al Multiparametric magnetic resonance imaging vs. standard care in men being evaluated for prostate cancer: a randomized study[J]. Urologic Oncology: Seminars and Original Investigations, 2015, 33 (1): 17.e1- 17.e7
doi: 10.1016/j.urolonc.2014.09.013
7 FüTTERER J J, BRIGANTI A, DE VISSCHERE P, et al Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? a systematic review of the literature[J]. European Urology, 2015, 68 (6): 1045- 1053
doi: 10.1016/j.eururo.2015.01.013
8 王建业, 陈鑫, 刘明, 等 MRI在前列腺癌诊断和治疗中的应用[J]. 磁共振成像, 2010, 1 (4): 253- 256
WANG Jian-ye, CHEN Xin, LIU Ming, et al MRI in clinical diagnosis and treatment of prostate cancer[J]. Chinese Journal of Magnetic Resonance Imaging, 2010, 1 (4): 253- 256
9 KOHL S, BONEKAMP D, SCHLEMMER H P, et al. Adversarial networks for the detection of aggressive prostate cancer [DB/OL]. [2017-02-26]. https://arxiv.org/abs/1702.08014.
10 蒋宏达, 叶西宁 一种改进的I-Unet网络的皮肤病图像分割算法[J]. 现代电子技术, 2019, 42 (12): 52- 56
JIANG Hong-da, YE Xi-ning An improved skin disease image segmentation algorithm based on I-Unet network[J]. Modern Electronics Technique, 2019, 42 (12): 52- 56
11 赖小波, 许茂盛, 徐小媚 多分类CNN的胶质母细胞瘤多模态MR图像分割[J]. 电子学报, 2019, 47 (8): 140- 149
LAI Xiao-bo, XU Mao-sheng, XU Xiao-mei Glioblastoma multiforme nulti-modal MRI images segmentation using multi-class CNN[J]. Acta Electronica Sinica, 2019, 47 (8): 140- 149
12 邢波涛, 李锵, 关欣 改进的全卷积神经网络的脑肿瘤图像分割[J]. 信号处理, 2018, 34 (8): 911- 922
XING Bo-tao, LI Qiang, GUAN Xin A brain tumor image segmentation method based on improved fully convolutional neural network[J]. Journal of Signal Processing, 2018, 34 (8): 911- 922
13 田萱, 王亮, 丁琪 基于深度学习的图像语义分割方法综述[J]. 软件学报, 2019, 30 (2): 250- 278
TIAN Xuan, WANG Liang, DING Qi Review of image semantic segmentation based on deep learning[J]. Journal of Software, 2019, 30 (2): 250- 278
14 RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation [C] // International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer, 2015: 234-241.
15 HE K, GKIOXARI G, DOLLáR P, et al. Mask R-CNN [C] // IEEE International Conference on Computer Vision. Washington D. C. : IEEE, 2017: 2961-2969.
16 TSEHAY Y K, LAY N S, ROTH H R, et al. Convolutional neural network based deep-learning architecture for prostate cancer detection on multi-parametric magnetic resonance images [C] // Medical Imaging 2017: Computer-Aided Diagnosis. Orlando: SPIE, 2017: 1013405.
17 YANG X, LIU C, WANG Z, et al Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI[J]. Medical Image Analysis, 2017, 42: 212- 227
doi: 10.1016/j.media.2017.08.006
18 YUAN Y, QIN W, BUYYOUNOUSKI M, et al Prostate cancer classification with multiparametric MRI transfer learning model[J]. Medical Physics, 2019, 46 (2): 756- 765
doi: 10.1002/mp.13367
19 HU J, SHEN L, SUN G. Squeeze-and-excitation networks [C] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7132-7141.
20 LIU P, WANG S, TURKBEY B, et al. A prostate cancer computer-aided diagnosis system using multimodal magnetic resonance imaging and targeted biopsy labels [C] // Medical Imaging 2013: Computer-Aided Diagnosis. Lake Buena Vista: SPIE, 2013: 86701G.
21 YU Q, LIU F, SONG Y Z, et al. Sketch me that shoe [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 799-807.
22 QIAN C, WANG L, GAO Y, et al In vivo MRI based prostate cancer localization with random forests and auto-context model[J]. Computerized Medical Imaging and Graphics, 2016, 52: 44- 57
doi: 10.1016/j.compmedimag.2016.02.001
23 MAZZETTI S, GIANNINI V, RUSSO F, et al Computer-aided diagnosis of prostate cancer using multi-parametric MRI: comparison between PUN and Tofts models[J]. Physics in Medicine and Biology, 2018, 63 (9): 095004
doi: 10.1088/1361-6560/aab956
24 MILLETARI F, NAVAB N, AHMADI S A. V-net: fully convolutional neural networks for volumetric medical image segmentation [C]// 2016 4th International Conference on 3D Vision. Stanford: IEEE, 2016: 565-571.
25 ISHIOKA J, MATSUOKA Y, UEHARA S, et al Computer‐aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm[J]. BJU International, 2018, 122 (3): 411- 417
doi: 10.1111/bju.14397
26 陆洋, 柏根基 定量动态增强磁共振成像及其在前列腺癌诊断和治疗中的研究进展[J]. 磁共振成像, 2015, 6 (10): 796- 800
LU Yang, BO Gen-ji Progress of quantitative dynamic contrast-enhanced magnetic resonance imaging and in diagnosis and evaluation on the therapeutic response of prostate cancer[J]. Chinese Journal of Magnetic Resonance Imaging, 2015, 6 (10): 796- 800
doi: 10.3969/j.issn.1674-8034.2015.10.016
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