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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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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        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 0.654 0.695 0.970 0.685
SE-特征图串联 0.633 0.722 0.960 0.634
SE-特征图串联 0.633 0.726 0.960 0.630
SE-特征图串联 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
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