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收稿：2020-08-22，
修回：2021-1-13，
录用：2021-1-20，
纸质出版：2021-07-16
移动端阅览
目的
2
肝脏肿瘤是人体最具侵袭性的恶性肿瘤之一，传统的肿瘤诊断依靠观察患者的CT（computed tomography）图像，工作量大时易造成疲劳，难免会产生误诊，为此使用计算机辅助的方法进行诊断，但现有的深度学习方法中存在肿瘤分类准确率低、网络的特征表达能力和特征提取能力较弱等问题。对此，本文设计了一种多尺度深度特征提取的分类网络模型。
方法
2
首先在原始CT图像中选取感兴趣区域，然后根据CT图像的头文件进行像素值转换，并进行数据增强来扩充构建数据集，最后将处理后的数据输入到本文提出的分类网络模型中输出分类结果。该网络通过多尺度特征提取模块来提取图像的多尺度特征并增加网络的感受野，使用深度特征提取模块降低背景噪声信息，并着重关注病灶区域有效特征，通过集成并行的空洞卷积使得尺度多元化，并将普通卷积用八度卷积替换来减少参数量，提升分类性能，最终实现了对肝脏肿瘤的精确分类。
结果
2
本文模型达到了87.74%的最高准确率，比原始模型提升了9.92%；与现有主流分类网络进行比较，多项评价指标占优，达到了86.04%的召回率，87%的精准率，86.42%的F1分数；此外，通过消融实验进一步验证了所提方法的有效性。
结论
2
本文方法可以较为准确地对肝脏肿瘤进行分类，将此方法结合到专业的医疗软件当中去，能够为医生早期的诊断和治疗提供可靠依据。
Objective
2
Liver tumors are the most aggressive malignancies in the human body. The definition of lesion type and lesion period based on computed tomography(CT) images determines the diagnosis and strategy of the treatment
which requires professional knowledge and rich experience of experts to classify them. Fatigue is easily experienced when the workload is heavy
and even experienced senior experts have difficulty avoiding misdiagnosis. Deep learning can avoid the drawbacks of traditional machine learning that takes a certain amount of time to manually extract the features of the image and perform dimensionality reduction
and is capable of extracting high-dimensional features of an image. Using deep learning to assist doctors in diagnosis is important. In the existing medical image classification task
the challenge of the low accuracy of tumor classification
the weak capability of the feature extraction
and the rough dataset still remain. To address these tasks
this study presents a method with a multi-scale and deep feature extraction classification network.
Method
2
First
we extract the region of interest (ROI) according to the contours of the liver tumors that were labeled by experienced radiologists
along with the ROI of healthy livers. The ROI is extracted to capture the features of the lesion area and surrounding tissue
which is relative to the size of the lesion. Due to the different sizes of the lesion area
the size of the extracted ROI is also different. Then
the pixel value is converted and data augmentation is performed. The dataset is Hounsfield windows
the range of CT values is (-1 024
3 071)
and the range of digital imaging and communications in medicine(DICOM) image is (0
4 096). The pixel values of DICOM images have to be converted to CT values. First
we read rescale_intercept and rescale_slope from the DICOM header file
and then we use the formula to convert. Thereafter
we limit the CT values of liver datasets to [-100
400] Hounsfield HU to avoid the influence of the background noise of the unrelated organs or tissues. We perform several data augmentation methods such as flipping
rotation
and transforming to expand the diversity of the datasets. Then
these images are sent into the MD_SENet for classification. The MD_SENet network is a SE_ResNet-like convolution neural network that can achieve end-to-end classification. The SE_ResNet learns the important features automatically from each channel to strengthen the useful features and suppress useless ones. MD_SENet network is much deeper than SE_ResNet. Our contributions are the following: 1) Hierarchical residual-like connections are used to improve multi-scale expression and increase the receptive field of each network layer. In the study
the image features after 1×1 convolution layers are divided into four groups. Each group of features passes through the 3×3 residual-like convolution groups
which improves the multi-scale feature extraction of networks and enhances the acquisition of focus areas features. 2) Channel attention and spatial attention are used to further focus on effective information on medical images. We let the feature images first go through the channel attention module
then we multiply its input and output to go through the spatial attention module. Then
we multiply the output of the spatial attention module and its input
which can pay more attention to the features of the lesion area and reduce the influence of background noise. 3) Atrous convolutions connected in parallel which refer to the spatial pyramid pooling
then we use 1×1 convolution layers to strengthen the feature. Finally
we concatenate the output and use softmax in classification. In this way
we can expand the receptive field and increase the image resolution
which can improve the feature expression ability and prevent the loss of information effectively. 4) The ordinary convolution is replaced by octave convolution to reduce the number of parameters and improve the classification performance. In this study
we compared the results of DenseNet
ResNet
MnasNet
MobileNet
ShuffleNet
SK_ResNet
and SE_ResNet with those of our MD_SENet
all of which were trained on the liver dataset. During the experiment
due to the limitation of graphics processing unit(GPU) memory
we set a batch size of 16 with Adam optimization and learning rate of 0.002 for 150 epochs. We used the dataset in Pytorch framework
Ubuntu 16.04. All experiments used the NVIDIA GeForce GTX 1060 Ti GPU to verify the effectiveness of our proposed method.
Result
2
Our training set consists of 4 096 images and the test set consists of 1 021 images for the liver dataset. The classification accuracy of our proposed method is 87.74% and is 9.92% higher than the baseline (SE_ResNet101). Our module achieves the best result compared with the state-of-the-art network and achieved 86.04% recall
87% precision
86.42% F1-score under various evaluation indicators. Ablation experiments are conducted to verify the effectiveness of the method.
Conclusion
2
In this study
we proposed a method to classify the liver tumors accurately. We combined the method into professional medical software so that we can provide a foundation that physicians can use in early diagnosis and treatment.
Al-Kadi O S. 2008. Combined statistical and model based texture features for improved image classification//Proceedings of the 4th IET International Conference on Advances in Medical, Signal and Information Processing. MEDSIP, Italy: IEEE: 175-178[ DOI: arXiv:1512.08814 http://dx.doi.org/arXiv:1512.08814 ]
Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A. 2018. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4): #834[ DOI: arXiv:1606.00915v2 http://dx.doi.org/arXiv:1606.00915v2 ]
Chen Y P, Fang H Q, Xu B, Yan Z C, Yannis, Marcus, Yan S C and Feng J S. 2019. Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea(South): IEEE: 3434-3443[ DOI: 10.1109/ICCV.2019.00353 http://dx.doi.org/10.1109/ICCV.2019.00353 ]
Christ P F, Elshaer M E A, Ettlinger F, Tatavarty S, Bickel M, Bilic P, Rempfler M, Armbruster M, Hofmann F, Anastasi M, Sommer W H, Ahmadi S A and Menze B H. 2016. Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3d conditional random fields//Proceedings of the 19th International Conference on Medical Image Computing and Computer Assisted Intervention. Granada, Spain: Springer Verlag: 415-423[ DOI: 10.1007/978-3-319-46723-8_48 http://dx.doi.org/10.1007/978-3-319-46723-8_48 ]
Gao S H, Cheng M M, Zhao K, Zhang X Y, Yang M H and Philip H.S.T. 2019. Res2 Net: a new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence. #2938758[ DOI: 10.1109/tpami.2019.2938758 http://dx.doi.org/10.1109/tpami.2019.2938758 ]
Ghoneim A, Muhammad G and Hossain M S. 2020. Cervical cancer classification using convolutional neural networks and extreme learning machines. Future Generation Computer Systems, 102: 643-649[DOI: https://doi.org/10.1016/j.future.2019.09.015]
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE: 770-778[ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]
He T, Zhang Z, Zhang H, Zhang Z Y, Xie J Y and Li M. 2019. Bag of tricks for image classification with convolutional neural networks//Proceedings of the 32nd IEEE Conference on Computer Vision and Pattern Recognition. Los Angeles, USA: IEEE: 558-567
Hu J, Shen L and Sun G. 2018. Squeeze-and-excitation networks//Proceedings of the 31st IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 7132-7141[ DOI: 10.1109/CVPR.2018.00745 http://dx.doi.org/10.1109/CVPR.2018.00745 ]
Huang G, Liu Z, Maaten L V D, Weinberger and Kilian Q. 2017. Densely connected convolutional networks//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE: 2261-2269[ DOI: 10.1109/CVPR.2017.243 http://dx.doi.org/10.1109/CVPR.2017.243 ]
Kingma D and Ba J. 2015. Adam: a method for stochastic optimization//Proceedings of the 3rd International Conference on Learning Representations. San Diego, USA: ICLR: 1-15
Liu Z, Zhang X L, Song Y Q, Zhu Y and Yuan D Q. 2018. Liver segmentation with improved U-Net and Morphsnakes algorithm. Journal of Image and Graphics, 23(8): 1254-1262
刘哲, 张晓林, 宋余庆, 朱彦, 袁德琪. 2018. 结合改进的U-Net和Morphsnakes的肝脏分割. 中国图象图形学报, 23(8): 1254-1262)[DOI: 10.11834/jig. 170585]
Li X, Wang W H, Hu X L and Yang J. 2019. Selective kernel networks//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). California, USA: IEEE: 510-519[ DOI: 10.1109/CVPR.2019.00060 http://dx.doi.org/10.1109/CVPR.2019.00060 ]
Litjens G, Kooi T, Bejnordi B E, Setio A A A, Ciompi F, Ghafoorian M, Jeroen A M, Ginneken B and Sanchez C. 2017. A survey on deep learning in medical image analysis. Medical Image Analysis, 42: 60-88[DOI: 10.1016/j.media.2017.07.005]
Nei Y N. 2012 Research on Application of Support Vector Machine in Liver B Ultrasound Images Classification. Xi'an: Xi'an University of Science and Technology
聂亚娜. 2012. 支持向量机在肝脏B超图像分类中的应用研究. 西安: 西安科技大学
Romero F P, Diler A, Bisson-Gregoire G, Turcotte S, Lapointe R, Vandenbroucke-Menu F, Tang A and Kadoury S. 2019. End-to-end discriminative deep network for liver lesion classification//Proceedings of the 16th IEEE International Symposium on Biomedical Imaging. Venice, Italy: IEEE: 1243-1246[ DOI: 10.1109/ISBI.2019.8759257 http://dx.doi.org/10.1109/ISBI.2019.8759257 ]
Sandler M, Howard A, Zhu M, Zhmoginov A and Chen L C. 2018. MobileNetV2: Inverted residuals and linear bottlenecks//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, USA: IEEE: 4510-4520[ DOI: 10.1109/CVPR.2018.00474 http://dx.doi.org/10.1109/CVPR.2018.00474 ]
Szegedy C, Ioffe S, Vanhoucke V and Alemi, A. 2016. Inception-v4, Inception-ResNet and the impact of residual connections on learning//Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco, USA: AAAI: 4278-4284
Tan M X, Chen B, Pang R M and Vasudevan V. 2018. MnasNet: platform-aware neural architecture search for mobile//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, USA: IEEE: 2815-2823[ DOI: arXiv:1807.11626 http://dx.doi.org/arXiv:1807.11626 ]
Woo S, Park J, Lee J Y and Kweon I S. 2018. CBAM: convolutional block attention module//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer Verlag: 3-19[ DOI: 10.1007/978-3-030-01234-2_1 http://dx.doi.org/10.1007/978-3-030-01234-2_1 ]
Zhang J P, Xie Y T, Wu Q and Xia Y. 2019. Medical image classification using synergic deep learning. Medical Image Analysis. 54: 10-19[ DOI: 10.1016/j.media.2019.02.010 http://dx.doi.org/10.1016/j.media.2019.02.010 ]
Zhang X Y, Zhou X Y, Lin M X and Sun J. 2017. ShuffleNet: an extremely efficient convolutional neural network for mobile devices//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 6848-6856[ DOI: 10.1109/CVPR.2018.00716 http://dx.doi.org/10.1109/CVPR.2018.00716 ]
Zou L. 2015 Research on Medical Image Classification Based On Twin Support Vector Machine. Gansu: Northwest Normal University
邹丽, 2015. 基于对支持向量机的医学图像分类研究. 甘肃: 西北师范大学
Zuckerman A J and Ji Q Q. 1983. Prevention of primary liver cancer-report on a meeting of the world health organization's scientific team. Guangzhou post-medical information. 1983(Z1): 131-132
吉千秋. 1983. 原发性肝癌的预防-世界卫生组织科研组的一次会议报告. 广后医学资料, 1983(Z1): 131-132
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