[1]赵浩辉,高永彬,杨淑群,等.基于条件卷积与注意力的肝脏分割算法[J].中国医学物理学杂志,2023,40(6):701-708.[doi:DOI:10.3969/j.issn.1005-202X.2023.06.006]
　ZHAO Haohui,GAO Yongbin,YANG Shuqun,et al.Liver segmentation algorithm based on conditional parametric attention network[J].Chinese Journal of Medical Physics,2023,40(6):701-708.[doi:DOI:10.3969/j.issn.1005-202X.2023.06.006]
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基于条件卷积与注意力的肝脏分割算法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第6期
页码:
701-708
栏目:
医学影像物理
出版日期:
2023-06-27

文章信息/Info

Title:
Liver segmentation algorithm based on conditional parametric attention network
文章编号:
1005-202X（2023）06-0701-08
作者:
赵浩辉1; 高永彬1; 杨淑群1; 胡小军2; 范应方3
1.上海工程技术大学电子电气工程学院， 上海 201620； 2.南方医科大学第五附属医院肝胆外科， 广东 广州 510000； 3.南方医科大学珠江医院肝胆一科， 广东 广州 510000
Author(s):
ZHAO Haohui1;  GAO Yongbin1;  YANG Shuqun1;  HU Xiaojun2;  FAN Yingfang3
1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Department of Hepatobiliary Surgery, the Fifth Affiliated Hospital of Southern Medical University, Guangzhou 510000, China 3. Hepatobiliary Department 1, Zhujiang Hospital of Southern Medical University, Guangzhou 510000, China
关键词:
肝脏分割; 卷积神经网络; 条件参数化卷积; CPat-Net
Keywords:
Keywords: liver segmentation convolutional neural network conditional parametric convolution CPat-Net
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2023.06.006
文献标志码:
A
摘要:
鉴于现有肝脏CT影像分割算法中存在的对比度较低、边界模糊、分割效果差问题，提出一种基于条件参数化卷积与注意力的分割网络（CPat-Net）。首先用条件参数化卷积替代残差网络中的常规卷积，其次将融合后的条件残差卷积模块集成至编码器中，用以提升模型容量和保持高效计算。然后利用特征注意（CPat）模块中的空间和通道注意力获取特征图的语义和细节信息，从而将局部特征与其全局依赖性更好地结合起来，最后利用深度监督进行多尺度语义信息的融合，提升方法的性能与鲁棒性。实验表明，在肝脏CT影像数据集中本文方法的Dice相似系数、交并比、Jaccrad系数分别达到了94.1%、90.3%、92.4%。相较于UNet、CENet、CSNet等前沿方法，本文方法在肝部分割上的准确度更为优异。
Abstract:
Abstract: In view of the low contrast, fuzzy boundary and poor segmentation results in the existing liver CT image segmentation algorithms, a conditional parametric attention network (CPat-Net) is presented. The method uses conditional parametric convolution to replace the conventional convolution in the residual network, and integrates the fused conditional residual convolution module into the encoder for improving model capacity and maintaining efficient computation. Then the spatial and channel attention mechanisms in the CPat module are used to obtain the semantic and detail information of the feature map, so as to better combine the local features with their global dependencies, and finally depth supervision is adopted to fuse multi-scale semantic information for improving the segmentation performance and robustness. The experiment reveals that the method has a Dice similarity cofficient, intersection over union and Jaccrad coefficient of 94.1%, 90.3% and 92.4% on the liver CT image data set. Compared with the advanced methods such as UNet, CENet and CSNet, the proposed method has a higher accuracy in liver segmentation.

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备注/Memo

备注/Memo:
【收稿日期】2023-02-15 【基金项目】上海市“科技创新行动计划”社会发展科技攻关项目（21DZ1204900）；广州市科技计划项目（202206010093） 【作者简介】赵浩辉，硕士研究生，研究方向：医学图像处理，E-mail: gyzhh1010@163.com 【通信作者】范应方，博士，副教授，研究方向：图像处理、计算机软件，E-mail: fanyf068700@sina.com
更新日期/Last Update: 2023-06-28