Polyp Segmentation Network GLIA-Net Based on Deep Learning

doi:10.19678/j.issn.1000-3428.0062860

Abstract

Abstract: With the development of the convolutional neural network, current improved U-Net networks for polyp segmentation can effectively improve the accuracy of polyp segmentation.However, many parameters are introduced, increasing the model complexity and decreasing computational efficiency.A network GLIA-Net, with low complexity and high performance, is proposed to segment polyp regions in endoscopic images.Taking U-Net as the infrastructure, a global and local interactive attention fusion module is added after the double-layer convolution.Global attention is based on two learnable external memories, achieved through cascaded linear and normalization layers.Local attention is based on the local cross-channel interaction strategy, which replaces the full-connection layer with one-dimensional convolution, reducing the computational complexity while maintaining the network performance and accelerating the network computing speed.By combining the advantages of the Efficient Channel Attention(ECA) and External Attention(EA), local and global attention are fused without introducing many parameters and extensive computations.An attention mechanism is introduced in the dimensions of channel and space to extract rich multi-scale semantic information.The experimental results on the Kvasir dataset show that the Intersection over Union(IoU), Dice, and Volume Overlap Errors(VOE) of GLIA-Net are 69.4%, 80.7%, and 5.0%, respectively.Compared with ExfuseNet, SegNet, ResUNet, and other networks, GLIA-Net has improved segmentation accuracy while ensuring network computing efficiency.

Key words: convolutional neural network, U-Net network, polyp segmentation, endoscopic images, interactive attention fusion module

摘要： 随着卷积神经网络的发展，现有改进的息肉分割U-Net网络能有效提高息肉分割准确率，但引入了大量参数，导致模型复杂度增大且计算效率降低。提出具有低复杂度、高性能的网络GLIA-Net，用于分割内窥镜图像中的息肉区域。以U-Net为基础架构，在双层卷积后加入全局与局部交互式注意力融合模块。全局注意力基于2个可学习的外部储存器，通过2个级联的线性层和归一化层来实现。局部注意力基于局部跨通道交互策略，将一维卷积代替全连接层，在保证网络性能的同时降低计算复杂度，加快网络的计算速度。结合高效通道注意力和外部注意力的优点，在不引入过多参数量和计算量的前提下融合局部注意力和全局注意力，同时在通道与空间2个维度上引入注意力机制，提取丰富的多尺度语义信息。在Kvasir数据集上的实验结果表明，GLIA-Net的平均交并比、Dice、体积重叠误差分别为69.4%、80.7%和5.0%，与ExfuseNet、SegNet、ResUNet等网络相比，在保证网络计算效率的同时具有较优的分割精度。

关键词: 卷积神经网络, U-Net网络, 息肉分割, 内窥镜图像, 交互式注意力融合模块

CLC Number:

TP391

REN Lili, BIAN Xuan, WANG Guanglei, WANG Hongrui. Polyp Segmentation Network GLIA-Net Based on Deep Learning[J]. Computer Engineering, 2022, 48(12): 248-254.

任莉莉, 边璇, 王光磊, 王洪瑞. 基于深度学习的息肉分割网络GLIA-Net[J]. 计算机工程, 2022, 48(12): 248-254.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0062860

http://www.ecice06.com/EN/Y2022/V48/I12/248

Figures/Tables 9

References

[1] GUO Y B, MATUSZEWSKI B J.Polyp segmentation with fully convolutional deep dilation neural network[C]//Proceedings of Annual Conference on Medical Image Understanding and Analysis.Berlin, Germany:Springer, 2020:377-388.
[2] VEMURI A S.Survey of computer vision and machine learning in gastrointestinal endoscopy[EB/OL].[2021-08-25].https://arxiv.org/abs/1904.13307.
[3] PATEL R, PATEL S.A comprehensive study of applying convolutional neural network for computer vision[J].International Journal of Advanced Science and Technology, 2020, 29(6):2161-2174.
[4] 李煌, 王晓莉, 项欣光.基于文本三区域分割的场景文本检测方法[J].计算机科学, 2020, 47(11):142-147. LI H, WANG X L, XIANG X G.Scene text detection based on triple segmentation[J].Computer Science, 2020, 47(11):142-147.(in Chinese)
[5] SAFAROV S, WHANGBO T K.A-DenseUNet:adaptive densely connected UNet for polyp segmentation in colonoscopy images with atrous convolution[J].Sensors, 2021, 21(4):1441.
[6] RONNEBERGER O, FISCHER P, THOMAS B.U-Net:convolutional networks for biomedical image segmentation[EB/OL].[2021-08-25].https://arxiv.org/pdf/1505.04597.pdf.
[7] WEI J, HU Y W, ZHANG R M, et al.Shallow attention network for polyp segmentation[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention.Berlin, Germany:Springer, 2021:699-708.
[8] 刘佳伟, 刘巧红, 李晓欧, 等.一种改进的双U型网络的结肠息肉分割方法[J].光学学报, 2021, 41(18):72-80. LIU J W, LIU Q H, LI X O, et al.Improved colonic polyp segmentation method based on double U-shaped network[J].Acta Optica Sinica, 2021, 41(18):72-80.(in Chinese)
[9] JHA D, RIEGLER M A, JOHANSEN D, et al.DoubleU-net:a deep convolutional neural network for medical image segmentation[C]//Proceedings of the 33rd International Symposium on Computer-Based Medical Systems.Washington D.C., USA:IEEE Press, 2020:558-564.
[10] 王亚刚, 郗怡媛, 潘晓英.改进DeepLabv3+网络的肠道息肉分割法[J].计算机科学与探索, 2020, 14(7):1243-1250. WANG Y G, XI Y Y, PAN X Y.Method for intestinal polyp segmentation by improving DeepLabv3+ network[J].Journal of Frontiers of Computer Science and Technology, 2020, 14(7):1243-1250.(in Chinese)
[11] POORNESHWARAN J M, SANTHOSH KUMAR S, RAM K, et al.Polyp segmentation using generative adversarial network[C]//Proceedings of the 41st Annual International Conference of Engineering in Medicine and Biology Society.Washington D.C., USA:IEEE Press, 2019:7201-7204.
[12] WANG Q L, WU B G, ZHU P F, et al.ECA-net:efficient channel attention for deep convolutional neural networks[C]//Proceedings of Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2020:11531-11539.
[13] GUO M H, LIU Z N, MU T J, et al.Beyond self-attention:external attention using two linear layers for visual tasks[EB/OL].[2021-08-25].https://arxiv.org/abs/2105.02358v2.
[14] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//Proceedings of Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:770-778.
[15] HU J, SHEN L, SUN G.Squeeze-and-excitation networks[C]//Proceedings of Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:7132-7141.
[16] VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.New York, USA:ACM Press, 2017:5998-6008.
[17] GLOROT X, BORDES A, BENGIO Y.Deep sparse rectifier neural networks[C]//Proceedings of the 14th International Conference on Artificial Intelligence and Statistics.New York, USA:[s.n.], 2011:315-323.
[18] ZHOU B L, KHOSLA A, LAPEDRIZA A, et al.Learning deep features for discriminative localization[C]//Proceedings of Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:2921-2929.
[19] ZHANG Z L, ZHANG X Y, PENG C, et al.ExFuse:enhancing feature fusion for semantic segmentation[EB/OL].[2021-08-25].https://arxiv.org/pdf/1804.03821.pdf.
[20] BADRINARAYANAN V, KENDALL A, CIPOLLA R.SegNet:a deep convolutional encoder-decoder architecture for image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12):2481-2495.
[21] HAN X.Automatic liver lesion segmentation using a deep convolutional neural network method[EB/OL].[2021-08-25].https://arxiv.org/pdf/1704.07239.pdf.
[22] ALOM M Z, YAKOPCIC C, TAHA T M, et al.Nuclei segmentation with recurrent residual convolutional neural networks based U-net(R2U-Net)[C]//Proceedings of National Aerospace and Electronics Conference.Washington D.C., USA:IEEE Press, 2018:228-233.
[23] ZHOU Z W, RAHMAN SIDDIQUEE M M, TAJBAKHSH N, et al.UNet++:a nested U-Net architecture for medical image segmentation[C]//Proceedings of International Workshop on Deep Learning in Medical Image Analysis.Berlin, Germany:Springer, 2018:3-11.
[24] HUANG H M, LIN L F, TONG R F, et al.UNet 3+:a full-scale connected UNet for medical image segmentation[C]//Proceedings of International Conference on Acoustics, Speech and Signal Processing.Washington D.C., USA:IEEE Press, 2020:1055-1059.
[25] CHEN L C, ZHU Y K, PAPANDREOU G, et al.Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2018:833-851.
[26] POZDEEV A A, OBUKHOVA N A, MOTYKO A A.Automatic analysis of endoscopic images for polyps detection and segmentation[C]//Proceedings of Conference of Russian Young Researchers in Electrical and Electronic Engineering.Washington D.C., USA:IEEE Press, 2019:1216-1220.
[27] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al.An image is worth 16x16 words:Transformers for image recognition at scale[C]//Proceedings of International Conference on Learning Representations.San Diego, USA:[s.n.], 2021:1-9.

Please choose a citation manager

Content to export