浏览全部资源
扫码关注微信
收稿日期：2021-04-06，
修回日期：2021-05-07，
录用日期：2021-5-15，
纸质出版日期：2021-09-16
移动端阅览
目的
2
肝纤维化是众多慢性肝脏疾病的常见表现，如不及时治疗可发展为肝硬化甚至引发肝癌。肝纤维化的准确评估对临床治疗和预后评估等至关重要。目前，肝纤维化的诊断通过肝穿活检判断，有创且有并发症危险。为此，基于影像学的无创诊断方法越来越受到关注。本文提出一种基于通道注意力与空间注意力机制改进的用于肝纤维化区域的自动化分割U-Net（liver fibrosis region segmentation network based on spatial and channel attention mechanisms，LFSCA-UNet）。
方法
2
依据Attention U-Net的改进方式，围绕U-Net的跳跃连接结构进行基于注意力的改进，在AG（attention gate）的基础上，加入以ECA（efficient channel attention）模块为实现方式的通道注意力机制，依据加入ECA的位置，LFSCA-UNet分为A、B、C共3个子型。
结果
2
在肝数据集上与其他实验网络进行评估对比，本文提出的LFSCA-UNet网络结构平均Dice系数达到了93.33%，相比原始U-Net的Dice系数提高了0.539 6%。
结论
2
本文方法将空间注意力机制与通道注意力机制进行结合，有效提高了肝纤维化区域的分割精度，对空间注意力模块使用通道注意力模块优化输入和输出，增加了网络的稳定性，提升了网络的整体效果。
Objective
2
Liver fibrosis is a common manifestation of many chronic liver diseases. It can develop into cirrhosis and even lead to liver cancer if not treated in time. The early diagnosis of liver fibrosis helps prevent the occurrence of severe liver disease. Studies have shown that timely and correct treatment can reverse liver fibrosis and even cirrhosis. Therefore
the accurate assessment of liver fibrosis is essential to the clinical treatment and prognosis assessment of liver fibrosis. At present
the diagnosis of liver fibrosis in the medical field is evaluated through liver biopsy
which is generally a safe procedure but invasive. The complications of liver biopsy are rare but potentially lethal
so noninvasive diagnosis methods based on imaging have attracted considerable interest.
Method
2
This paper proposes a network for the segmentation of liver fibrosis regions
called LFSCA-UNet(liver fibrosis region segmentation network based on spatial and channel attention mechanisms-UNet). It has improved the U-Net with two different attention mechanisms. U-Net is a convolutional neural network used for image semantic segmentation. Attention U-Net is an improved version of U-Net
it adds a group of attention gate modules into each skip connection of the original U-Net. The attention gate modules in attention U-Net is a spatial attention mechanism. LFSCA-UNet adds a channel attention mechanism to each skip connection structure. In this study
the efficient channel attention(ECA)
which is a channel attention mechanism based on the squeeze and excitation network
was used in implementing the added mechanism. The core idea of the squeeze and excitation network is to allow networks to automatically learn dependencies between channels. This network changes a conventional convolution layer to a convolution layer with a squeeze and excitation block
which can be divided into two parts: squeeze and excitation. The squeezing part uses global pooling to obtain a feature vector of a current convolutional layer feature map
whereas the excitation part uses two fully connected layers with different numbers. The first drop of the dimension and the second upgraded
and finally
the weight of each channel is obtained after sigmoid activation
which is multiplied by the original feature map as the input of the subsequent layer of the network. The efficient channel attention block is an improvement of the squeeze and excitation block
which removes the part of reducing dimension and uses 1 d convolution instead of the fully connected layer. It has better performance and fewer parameters. The CT(computed tomography) images used in this study was obtained from 88 patients with liver fibrosis and provided by the Department of Liver Surgery
Renji Hospital
Shanghai Jiao Tong University School of Medicine. One Nvidia Tesla P100 graphics cards with 16 GB memory were used in training networks
and Python 3.8.5 and PyTorch 1.7.1 were used.
Result
2
This paper horizontally compared five different experimental networks according to five different indicators
namely
Dice coefficient
Jaccard index
precision
recall (sensitivity)
and specificity. LFSCA-UNet gets the highest result of mean Dice coefficient (0.933 3)
better than the original U-Net (0.539 6%).
Conclusion
2
This paper verifies that the combination of spatial attention and channel attention mechanisms can effectively improve the segmentation result of liver fibrosis. For the spatial attention module
using the channel attention module in optimizing inputs can increase network stability and optimizing outputs can improve the overall effect of the network.
Cai L, Gao J and Zhao D. 2020. A review of the application of deep learning in medical image classification and segmentation. Annals of Translational Medicine, 8(11): #713[DOI: 10.21037/atm.2020.02.44]
Chen H Y, Gao J Y, Zhao D, Wang H Z, Song H and Su Q H. 2021. Review of the research progress in deep learning and biomedical image analysis till 2020. Journal of Image and Graphics, 26(3): 475-486
陈弘扬, 高敬阳, 赵地, 汪红志, 宋红, 苏庆华. 2021. 深度学习与生物医学图像分析2020年综述. 中国图象图形学报, 26(3): 475-486[DOI: 10.11834/jig.200351]
Choi K J, Jang J K, Lee S S, Sung Y S, Shim W H, Kim H S, Yun J, Choi J Y, Lee Y, Kang B K, Kim J H, Kim S Y and Yu E S. 2018. Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced CT images in the liver. Radiology, 289(3): 688-697[DOI: 10.1148/radiol.2018180763]
Dice L R. 1945. Measures of the amount of ecologic association between species. Ecology, 26(3): 297-302[DOI: 10.2307/1932409]
Ellis E L and Mann D A. 2012. Clinical evidence for the regression of liver fibrosis. Journal of Hepatology, 56(5): 1171-1180[DOI: 10.1016/j.jhep.2011.09.024]
Hu J, Shen L and Sun G. 2018. Squeeze-and-excitation networks//Proceedings of 2018 IEEE/CVF 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 ]
Levandowsky M and Winter D. 1971. Distance between sets. Nature, 234(5323): 34-35[DOI: 10.1038/234034a0]
Li Q J, Guo Q Y, Chen H B and Zhang R G. 2018. Research progress of liver fibrosis based on texture analysis and deep learning. Radiologic Practice, 33(10): 997-1001
李秋菊, 郭启勇, 陈海斌, 张荣国. 2018. 基于纹理分析和深度学习的肝纤维化研究进展. 放射学实践, 33(10): 997-1001[DOI: 10.13609/j.cnki.1000-0313.2018.10.004]
Liu G C and Liu P F. 2021. Progress in quantitative assessment of liver fibrosis and cirrhosis by MRI. Chinese Journal of Magnetic Resonance Imaging, 12(5): 114-117
刘冠辰, 刘鹏飞. 2021. MRI对肝纤维化及肝硬化定量评估的研究进展. 磁共振成像, 12(5): 114-117[DOI: 10.12015/issn.1674-8034.2021.05.028]
Liu H, Guo S L, Du F H and Wang H. 2012. Progress of CT and MRI in chronic liver fibrosis. Chinese Journal of CT and MRI, 10(1): 74-76
刘宏, 郭顺林, 杜富会, 王浩. 2012. CT和MRI对慢性肝纤维化的研究进展. 中国CT和MRI杂志, 10(1): 74-76[DOI: 10.3969/j.issn.1672-5131.2012.01.024]
Liu P Y, Hao F E, Liu A S and Zhao L. 2019. The research progress of CT in evaluating the course of cirrhosis. Computerized Tomography Theory and Applications, 28(6): 753-761
刘沛瑶, 郝粉娥, 刘挨师, 赵磊. 2019. CT在评价肝硬化病程方面的研究进展. CT理论与应用研究, 28(6): 753-761[DOI: 10.15953/j.1004-4140.2019.28.06.14]
Mallat A and Lotersztajn S. 2013. Cellular mechanisms of tissue fibrosis. 5. Novel insights into liver fibrosis. American Journal of Physiology-Cell Physiology, 305(8): C789-C799[DOI: 10.1152/ajpcell.00230.2013]
Oktay O, Schlemper J, Le Folgoc L, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla N Y, Kainz B, Glocker B and Rueckert D. 2018. Attention U-Net: learning where to look for the pancreas[EB/OL]. [2021-04-08] . https://arxiv.org/pdf/1804.03999.pdf https://arxiv.org/pdf/1804.03999.pdf
Ronneberger O, Fischer P and Brox T. 2015. U-net: convolutional networks for biomedical image segmentation//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer: 234-241[ DOI: 10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]
Selvaraju R R, Cogswell M, Das A, Vedantam R, Parikh D and Batra D. 2017. Grad-CAM: visual explanations from deep networks via gradient-based localization//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 618-626[ DOI: 10.1109/iccv.2017.74 http://dx.doi.org/10.1109/iccv.2017.74 ]
Shi J L. 2021. Observation on application effects of color doppler ultrasound and CT in the evaluation of liver morphological changes in patients with chronic liver fibrosis. Chinese Journal of CT and MRI, 19(5): 51-53
史景璐. 2021. 彩超和CT在慢性肝纤维化患者肝脏形态学变化评估中的应用效果观察. 中国CT和MRI杂志, 19(5): 51-53[DOI: 10.3969/j.issn.1672-5131.2021.05.018]
Wang Q L, Wu B G, Zhu P F, Li P H, Zuo W M and Hu Q H. 2020. ECA-net: efficient channel attention for deep convolutional neural networks//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 11531-11539[ DOI: 10.1109/CVPR42600.2020.01155 http://dx.doi.org/10.1109/CVPR42600.2020.01155 ]
West J and Card T R. 2010. Reduced mortality rates following elective percutaneous liver biopsies. Gastroenterology, 139(4): 1230-1237[DOI: 10.1053/j.gastro.2010.06.015]
Zeng M D, Wang T L and Wang B E. 2002. Common understanding of diagnosis and effect of hepatic fibrosis. Journal of Diagnostics Concepts and Practice, 1(3): 191-192
曾民德, 王泰龄, 王宝恩. 2002. 肝纤维化诊断和疗效评估共识. 诊断学理论与实践, 1(3): 191-192[DOI: 10.3969/j.issn.1671-2870.2002.03.020]
Zhao W M. 2019. Research on Evaluation Method of Liver Fibrosis by Ultrasound Images based on Radiomics and Deep Transfer Learning. Shenzhen: Shenzhen University
赵万明. 2019. 基于影像组学和深度迁移学习的超声图像肝纤维化评估方法研究. 深圳: 深圳大学
相关作者
相关机构
京公网安备11010802024621