Coordinate Attention Residual Deformable U-Net for Vessel Segmentation

被引:5
作者
Wu, Cong [1 ]
Liu, Xiao [1 ]
Li, Shijun [1 ]
Long, Cheng [1 ]
机构
[1] Hubei Univ Technol, Wuhan, Peoples R China
来源
NEURAL INFORMATION PROCESSING, ICONIP 2021, PT III | 2021年 / 13110卷
关键词
Retinal vessel segmentation; U-Net; Coordinate Attention; DIABETIC-RETINOPATHY; NETWORK;
D O I
10.1007/978-3-030-92238-2_29
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The location information of features is essential for pixel-level segmentation tasks such as retinal vessel segmentation. In this study, we proposed the CARDU-Net (Coordinate Attention Gate Residual Deformable U-Net) model based on coordinate attention mechanism for the segmentation task, which can extract effective features by accurately locating feature location information and enhance the accuracy of segmentation. The deformable convolution and residual structure with Dropblock are also introduced to refine the encoder structure of U-Net. The model is applied to DRIVE, CHASE DB1, and LUNA (2017) datasets, and the experimental results on the three public datasets demonstrate the superior segmentation capability of CARDU-Net, and the modified part is reflected by ablation experiments in this work. The results show that the CARDU-Net model performs better compared to other network models and can segment medical images accurately.
引用
收藏
页码:345 / 356
页数:12
相关论文
共 21 条
[1]   Application of deep learning for retinal image analysis: A review [J].
Badar, Maryam ;
Haris, Muhammad ;
Fatima, Anam .
COMPUTER SCIENCE REVIEW, 2020, 35
[2]   Retinal Vascular Tortuosity, Blood Pressure, and Cardiovascular Risk Factors [J].
Cheung, Carol Yim-lui ;
Zheng, Yingfeng ;
Hsu, Wynne ;
Lee, Mong Li ;
Lau, Qiangfeng Peter ;
Mitchell, Paul ;
Wang, Jie Jin ;
Klein, Ronald ;
Wong, Tien Yin .
OPHTHALMOLOGY, 2011, 118 (05) :812-818
[3]   Deformable Convolutional Networks [J].
Dai, Jifeng ;
Qi, Haozhi ;
Xiong, Yuwen ;
Li, Yi ;
Zhang, Guodong ;
Hu, Han ;
Wei, Yichen .
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2017, :764-773
[4]   Automatic detection of diabetic retinopathy using an artificial neural network: A screening tool [J].
Gardner, GG ;
Keating, D ;
Williamson, TH ;
Elliott, AT .
BRITISH JOURNAL OF OPHTHALMOLOGY, 1996, 80 (11) :940-944
[5]  
Ghiasi G, 2018, Arxiv, DOI [arXiv:1810.12890, 10.48550/arXiv.1810.12890, DOI 10.48550/ARXIV.1810.12890]
[6]   CE-Net: Context Encoder Network for 2D Medical Image Segmentation [J].
Gu, Zaiwang ;
Cheng, Jun ;
Fu, Huazhu ;
Zhou, Kang ;
Hao, Huaying ;
Zhao, Yitian ;
Zhang, Tianyang ;
Gao, Shenghua ;
Liu, Jiang .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2019, 38 (10) :2281-2292
[7]   CHANNEL ATTENTION RESIDUAL U-NET FOR RETINAL VESSEL SEGMENTATION [J].
Guo, Changlu ;
Szemenyei, Marton ;
Hu, Yangtao ;
Wang, Wenle ;
Zhou, Wei ;
Yi, Yugen .
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, :1185-1189
[8]   Residual Spatial Attention Network for Retinal Vessel Segmentation [J].
Guo, Changlu ;
Szemenyei, Marton ;
Yi, Yugen ;
Zhou, Wei ;
Bian, Haodong .
NEURAL INFORMATION PROCESSING, ICONIP 2020, PT I, 2020, 12532 :509-519
[9]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778
[10]   Coordinate Attention for Efficient Mobile Network Design [J].
Hou, Qibin ;
Zhou, Daquan ;
Feng, Jiashi .
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, :13708-13717