Citation: | Cong-Zhong Wu, Jun Sun, Jing Wang, Liang-Feng Xu, Shu Zhan. Encoding-decoding Network With Pyramid Self-attention Module for Retinal Vessel Segmentation. International Journal of Automation and Computing, vol. 18, no. 6, pp.973-980, 2021. https://doi.org/10.1007/s11633-020-1277-0 |
[1] |
C. Kirbas, F. Quek. A review of vessel extraction techniques and algorithms. ACM Computing Surveys, vol. 36, no. 2, pp. 81–121, 2004. DOI: 10.1145/1031120.1031121.
|
[2] |
M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, S. A. Barman. Blood vessel segmentation methodologies in retinal images–a survey. Computer Methods and Programs in Biomedicine, vol. 108, no. 1, pp. 407–433, 2012. DOI: 10.1016/j.cmpb.2012.03.009.
|
[3] |
B. Al-Diri, A. Hunter, D. Steel. An active contour model for segmenting and measuring retinal vessels. IEEE Transactions on Medical Imaging, vol. 28, no. 9, pp. 1488–1497, 2009. DOI: 10.1109/TMI.2009.2017941.
|
[4] |
G. Azzopardi, N. Petkov. Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters. Pattern Recognition Letters, vol. 34, no. 8, pp. 922–933, 2013. DOI: 10.1016/j.patrec.2012.11.002.
|
[5] |
S. Aslani, H. Sarnel. A new supervised retinal vessel segmentation method based on robust hybrid features. Biomedical Signal Processing and Control, vol. 30, pp. 1–12, 2016. DOI: 10.1016/j.bspc.2016.05.006.
|
[6] |
L. Wang, A. Bhalerao, R. Wilson. Analysis of retinal vasculature using a multiresolution hermite model. IEEE Transactions on Medical Imaging, vol. 26, no. 2, pp. 137–152, 2007. DOI: 10.1109/TMI.2006.889732.
|
[7] |
Y. Yin, M. Adel, S. Bourennane. Retinal vessel segmentation using a probabilistic tracking method. Pattern Recognition, vol. 45, no. 4, pp. 1235–1244, 2012. DOI: 10.1016/j.patcog.2011.09.019.
|
[8] |
Q. Qi, Q. D. Li, Y. Q. Cheng, Q. Q. Hong. Skeleton marching-based parallel vascular geometry reconstruction using implicit functions. International Journal of Automation and Computing, vol. 17, no. 1, pp. 30–43, 2020. DOI: 10.1007/s11633-019-1189-4.
|
[9] |
W. J. Bai, M. Sinclair, G. Tarroni, O. Oktay, M. Rajchl, G. Vaillant, A. M. Lee, N. Aung, E. Lukaschuk, M. M. Sanghvi, F. Zemrak, K. Fung, J. M. Paiva, V. Carapella, Y. J. Kim, H. Suzuki, B. Kainz, P. M. Matthews, S. E. Petersen, S. K. Piechnik, S. Neubauer, B. Glocker, D. Rueckert. Human-level CMR image analysis with deep fully convolutional networks. [Online], Available: https://arxiv.org/abs/1710.09289v2, 2017.
|
[10] |
Z. W. He, L. Zhang, F. Y. Liu. DiscoStyle: Multi-level logistic ranking for personalized image style preference inference. International Journal of Automation and Computing, vol. 17, no. 5, pp. 637–651, 2020. DOI: 10.1007/s11633-020-1244-1.
|
[11] |
Y. R. Liang, Z. Y. Xiao. Image encryption algorithm based on compressive sensing and fractional DCT via polynomial interpolation. International Journal of Automation and Computing, vol. 17, no. 2, pp. 292–304, 2020. DOI: 10.1007/s11633-018-1159-2.
|
[12] |
F. Z. Liao, M. Liang, Z. Li, X. L. Hu, S. Song. Evaluate the malignancy of pulmonary nodules using the 3-D deep leaky noisy-OR network. IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3484–3495, 2019. DOI: 10.1109/TNNLS.2019.2892409.
|
[13] |
P. Moeskops, M. Veta, M. W. Lafarge, K. A. J. Eppenhof, J. P. W. Pluim. Adversarial training and dilated convolutions for brain MRI segmentation. In Proceedings of the 3rd International Workshop on Deep Learning in Medical Image Analysis and the 7th International Workshop on Multimodal Learning for Clinical Decision Support, Springer, Quebec City, Canada, pp. 56−64, 2017. DOI: 10.1007/978-3-319-67558-9_7.
|
[14] |
S. Wang, M. Zhou, Z. Y. Liu, Z. Y. Liu, D. S. Gu, Y. L. Zang, D. Dong, O. Gevaert, J. Tian. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Medical Image Analysis, vol. 40, pp. 172–183, 2017. DOI: 10.1016/j.media.2017.06.014.
|
[15] |
Z. Y. Liu. Retinal vessel segmentation based on fully convolutional networks. [Online], Available: https://arxiv.org/abs/1911.09915, 2019.
|
[16] |
Z. W. Gu, J. Cheng, H. Z. Fu, K. Zhou, H. Y. Hao, Y. T. Zhao, T. Y. Zhang, S. H. Gao, J. Liu. CE-Net: Context encoder network for 2D medical image segmentation. IEEE Transactions on Medical Imaging, vol. 38, no. 10, pp. 2281–2292, 2019. DOI: 10.1109/TMI.2019.2903562.
|
[17] |
L. Luo, D. Y. Xue, X. L. Feng. HybridNetSeg: A compact hybrid network for retinal vessel segmentation. [Online], Available: https://arxiv.org/abs/1911.09982, 2019.
|
[18] |
J. Son, S. J. Park, K. H. Jung. Retinal vessel segmentation in fundoscopic images with generative adversarial networks. [Online], Available: https://arxiv.org/abs/1706.09318, 2017.
|
[19] |
J. Hu, L. Shen, S. Albanie, G. Sun, E. H. Wu. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 8, pp. 2011–2023, 2020. DOI: 10.1109/TPAMI.2019.2913372.
|
[20] |
J. Fu, J. Liu, H. J. Tian, Y. Li, Y. J. Bao, Z. W. Fang, H. Q. Lu. Dual attention network for scene segmentation. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Long Beach, USA, pp. 3141−3149, 2019. DOI: 10.1109/CVPR.2019.00326.
|
[21] |
E. Shelhamer, J. Long, T. Darrell. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 640–651, 2017. DOI: 10.1109/TPAMI.2016.2572683.
|
[22] |
O. Ronneberger, P. Fischer, T. Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Munich, Germany, pp. 234-241, 2015. DOI: 10.1007/978-3-319-24574-4_28.
|
[23] |
N. Abraham, N. M. Khan. A novel focal Tversky loss function with improved attention U-Net for lesion segmentation. [Online], Available: https://arxiv.org/abs/1810.07842, 2018.
|
[24] |
M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, V. K. Asari. Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. [Online], Available: https://arxiv.org/abs/1802.06955, 2018.
|
[25] |
H. Z. Fu, J. Cheng, Y. W. Xu, D. W. K. Wong, J. Liu, X. C. Cao. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Transactions on Medical Imaging, vol. 37, no. 7, pp. 1597–1605, 2018. DOI: 10.1109/TMI.2018.2791488.
|
[26] |
L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 834–848, 2018. DOI: 10.1109/TPAMI.2017.2699184.
|
[27] |
Q. Zhang, Z. P. Cui, X. G. Niu, S. J. Geng, Y. Qiao. Image segmentation with pyramid dilated convolution based on ResNet and U-Net. In Proceedings of the 24th International Conference International Conference on Neural Information Processing, Springer, Guangzhou, China, pp. 364−372, 2017. DOI: 10.1007/978-3-319-70096-0_38.
|
[28] |
H. Y. Xia, W. F. Sun, S. X. Song, X. W. Mou. Md-Net: Multi-scale dilated convolution network for CT images segmentation. Neural Processing Letters, vol. 51, no. 3, pp. 2915–2927, 2020. DOI: 10.1007/s11063-020-10230-x.
|
[29] |
D. G. Xiao, P. Zhong. Image semantic segmentation using deep convolutional nets, fully connected conditional random fields, and dilated convolution. In Proceedings of the 21st IEEE International Conference on High Performance Computing and Communications; the 17th IEEE International Conference on Smart City; the 5th IEEE International Conference on Data Science and Systems, IEEE, Zhangjiajie, China, pp. 1872−1877, 2019. DOI: 10.1109/HPCC/SmartCity/DSS.2019.00258.
|
[30] |
X. L. Wang, R. Girshick, A. Gupta, K. M. He. Non-local neural networks. [Online], Available: https://arxiv.org/abs/1711.07971, 2017.
|
[31] |
H. Tang, H. Liu, D. Xu, P. H. S. Torr, N. Sebe. AttentionGAN: Unpaired image-to-image translation using attention-guided generative adversarial networks. [Online], Available: https://arxiv.org/abs/1911.11897, 2019.
|
[32] |
B. W. Li, X. J. Qi, T. Lukasiewicz, P. H. S. Torr. Controllable text-to-image generation. [Online], Available: https://arxiv.org/abs/1909.07083, 2019.
|
[33] |
O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz, B. Glocker, D. Rueckert. Attention U-Net: Learning where to look for the pancreas. [Online], Available: https://arxiv.org/abs/1804.03999, 2018.
|
[34] |
A. D. Hoover, V. Kouznetsova, M. Goldbaum. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp. 203–210, 2000. DOI: 10.1109/42.845178.
|
[35] |
M. Niemeijer, J. Staal, B. Van Ginneken, M. Loog, M. D. Abràmoff. Comparative study of retinal vessel segmentation methods on a new publicly available database. In Proceedings of SPIE 5370, Medical Imaging 2004: Image Processing, San Diego, USA, pp. 648-656, 2004. DOI: 10.1117/12.535349.
|
[36] |
S. Roychowdhury, D. D. Koozekanani, K. K. Parhi. Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 3, pp. 1118–1128, 2015. DOI: 10.1109/JBHI.2014.2335617.
|
[37] |
Q. L. Li, B. W. Feng, L. P. Xie, P. Liang, H. S. Zhang, T. F. Wang. A cross-modality learning approach for vessel segmentation in retinal images. IEEE Transactions on Medical Imaging, vol. 35, no. 1, pp. 109–118, 2016. DOI: 10.1109/TMI.2015.2457891.
|
[38] |
M. Melinscak, P. Prentasic, S. Loncaric. Retinal vessel segmentation using deep neural networks. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications, Berlin, Germany, pp. 577−582, 2015.
|
[39] |
P. Liskowski, K. Krawiec. Segmenting retinal blood vessels with deep neural networks. IEEE Transactions on Medical Imaging, vol. 35, no. 11, pp. 2369–2380, 2016. DOI: 10.1109/TMI.2016.2546227.
|
[40] |
J. T. Zhuang J. LadderNet: Multi-path networks based on U-Net for medical image segmentation. [Online], Available: https://arxiv.org/abs/1810.07810, 2018.
|