DBCU-Net: deep learning approach for segmentation of coronary angiography images

The International Journal of Cardiovascular Imaging - Tập 39 Số 8 - Trang 1571-1579
Yuqiang Shen1, Zhe Chen2, Jijun Tong2, Nan Jiang2, Yun Ning2
1The Fourth Affiliated Hospital Zhejiang University School of Medicine, Jinhua, China
2School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China

Tóm tắt

Từ khóa


Tài liệu tham khảo

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660

Moayyedi PM, Lacy BE, Andrews CN, Enns RA, Howden CW, Vakil N (2017) ACG and CAG clinical guideline: management of dyspepsia. Am J Gastroenterol 112:988–1013. https://doi.org/10.1038/ajg.2017.154

Dehkordi MT, Sadri S, Doosthoseini A (2011) A review of coronary vessel segmentation algorithms. J Med Signals Sensors 1:49. https://doi.org/10.4103/2228-7477.83519

Sukanya A, Rajeswari R, Subramaniam Murugan K (2020) Region based coronary artery segmentation using modified Frangi’s vesselness measure. Int J Imaging Syst Technol 30:716–730. https://doi.org/10.1002/ima.22412

Mabrouk S, Oueslati C, Ghorbel F (2017) Multiscale graph cuts based method for coronary artery segmentation in angiograms. Irbm 38:167–175. https://doi.org/10.1016/j.irbm.2017.04.004

Daoudi A, Mahmoudi S (2016) A fully automatic cardiac segmentation method using region growing technique in Proceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence, 103–108.

Kerkeni A, Benabdallah A, Manzanera A, Bedoui MH (2016) A coronary artery segmentation method based on multiscale analysis and region growing. Comput Med Imaging Graph 48:49–61. https://doi.org/10.1016/j.compmedimag.2015.12.004

Ma G, Yang J, Huang Y, Zhao H (2019) A novel automatic coronary artery segmentation method based on region growing with annular and spherical sector partition. J Med Imaging Health Inform 9:148–152. https://doi.org/10.1166/jmihi.2019.2553

Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.

Zheng S, Ristovski K, Farahat A, Gupta C (2017) Long short-term memory network for remaining useful life estimation, IEEE International Conference on Prognostics and Health Management (ICPHM): IEEE, 88–95

Cui Z, Ke R, Pu Z, Wang Y (2018) Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. Arxiv Preprint https://doi.org/10.48550/arXiv.1801.02143

Ciresan D, Giusti A, Gambardella L, Schmidhuber J (2012) Deep neural networks segment neuronal membranes in electron microscopy images. Adv Neural Inform Process Syst. https://doi.org/10.5555/2999325.2999452

Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation, in International Conference on Medical Image Computing and Computer-assisted Intervention: Springer, Cham, 234–241.

Blaiech AG, Mansour A, Kerkeni A, Bedoui MH, Ben Abdallah A (2019) Impact of enhancement for coronary artery segmentation based on deep learning neural network, Iberian Conference on Pattern Recognition and Image Analysis: Springer, Cham, 260–272

Shi X, Du T, Chen S, Zhang H, Guan C, Xu B (2020) UENet: a novel generative adversarial network for angiography image segmentationm in 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC): IEEE, 1612–1615.

Xian Z, Wang X, Yan S, Yang D, Chen J, Peng C (2020) Main coronary vessel segmentation using deep learning in smart medical. Math Prob Eng. https://doi.org/10.1155/2020/8858344

Fan J, Du C, Song S, Cong W, Hao A, Yang J (2019) Enhanced subtraction image guided convolutional neural network for coronary artery segmentation, in Chinese Conference on Image and Graphics Technologies: Springer, 625–632

Li R, Bian G, Zhou X, Xie X, Ni Z, Hou Z (2020) CAU-net: A novel convolutional neural network for coronary artery segmentation in digital substraction angiography, in International Conference on Neural Information Processing: Springer, Cham, 185–196

Samuel PM, Veeramalai T (2021) VSSC Net: vessel specific skip chain convolutional network for blood vessel segmentation. Comput Methods Prog Biomed. https://doi.org/10.1016/j.cmpb.2020.105769

Zhou C, Dinh TV, Kong H, Yap J, Yeo KK, Lee HK, Liang K (2021) Automated deep learning analysis of angiography video sequences for coronary artery disease. Arxiv Preprint https://doi.org/10.48550/arXiv.2101.12505

Yang S, Kweon J, Kim Y (2019) Major vessel segmentation on x-ray coronary angiography using deep networks with a novel penalty loss function, in International Conference on Medical Imaging with Deep Learning--extended Abstract Track.

Jun TJ, Kweon J, Kim Y, Kim D (2020) T-net: Nested encoder–decoder architecture for the main vessel segmentation in coronary angiography. Neural Netw 128:216–233. https://doi.org/10.1016/j.neunet.2020.05.002

Azad R, Asadi-Aghbolaghi M, Fathy M, Escalera S (2019) Bi-directional ConvLSTM U-Net with densley connected convolutions. In Proceedings of the IEEE/CVF international conference on computer vision workshops.

Song H, Wang W, Zhao S, Shen J, Lam K (2018) Pyramid dilated deeper convlstm for video salient object detection, in Proceedings of the European Conference on Computer Vision (ECCV): Springer, Cham, 715–731.

Zhang Z (2018) Improved adam optimizer for deep neural networks, In IEEE/ACM 26th International Symposium on Quality of Service (IWQOS): Springer International Publishing, 1–2.

Yu F, Zhao J, Gong Y, Wang Z, Li Y, Yang F, Dong B, Li Q, Zhang (2019) Annotation-free cardiac vessel segmentation via knowledge transfer from retinal images. In International Conference on Medical Image Computing and Computer-assisted Intervention: Springer, 714–722.

Zhang J, Wang G, Xie H, Zhang S, Huang N, Zhang S, Gu L (2020) Weakly supervised vessel segmentation in X-ray angiograms by self-paced learning from noisy labels with suggestive annotation. Neurocomputing 417:114–127. https://doi.org/10.1016/j.neuc