Abstract
Many deep learning based methods have been proposed for retinal vessel segmentation, however few of them focus on the connectivity of segmented vessels, which is quite important for a practical computer-aided diagnosis system on retinal images. In this paper, we propose an efficient network to address this problem. A U-shape network is enhanced by introducing a semantics-guided module, which integrates the enriched semantics information to shallow layers for guiding the network to explore more powerful features. Besides, a recursive refinement iteratively applies the same network over the previous segmentation results for progressively boosting the performance while increasing no extra network parameters. The carefully designed recursive semantics-guided network has been extensively evaluated on several public datasets. Experimental results have shown the efficiency of the proposed method.
This work was supported by National Natural Science Foundation of China (NSFC) under Grant 61772106, Grant 61702078 and Grant 61720106005, and by the Fundamental Research Funds for the Central Universities of China.
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References
Araújo, R.J., Cardoso, J.S., Oliveira, H.P.: A deep learning design for improving topology coherence in blood vessel segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 93–101. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_11
Fraz, M.M., et al.: Blood vessel segmentation methodologies in retinal images-a survey. Comput. Methods Programs Biomed. 108(1), 407–433 (2012)
Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)
Liu, J.J., Hou, Q., Cheng, M.M., Feng, J., Jiang, J.: A simple pooling-based design for real-time salient object detection. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Mosinska, A., Marquez-Neila, P., Koziński, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Conference on Computer Vision and Pattern Recognition (2018)
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29
Oliveira, A., Pereira, S., Silva, C.A.: Retinal vessel segmentation based on fully convolutional neural networks. Expert Syst. Appl. 112, 229–242 (2018)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (2007)
Owen, C.G., et al.: Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program. Invest. Ophthalmol. Vis. Sci. 50(5), 2004–2010 (2009)
Srinidhi, C.L., Aparna, P., Rajan, J.: Recent advancements in retinal vessel segmentation. J. Med. Syst. 41(4), 70 (2017)
Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Wang, B., Qiu, S., He, H.: Dual encoding u-net for retinal vessel segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 84–92. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_10
Wegner, J.D., Montoya-Zegarra, J.A., Schindler, K.: A higher-order CRF model for road network extraction. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2013)
Wu, Y., Xia, Y., Song, Y., Zhang, Y., Cai, W.: Multiscale network followed network model for retinal vessel segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 119–126. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_14
Xie, S., Tu, Z.: Holistically-nested edge detection. In: International Conference on Computer Vision (ICCV) (2015)
Xu, R., Ye, X., Jiang, G., Liu, T., Li, L., Tanaka, S.: Semantics and multi-scale aggregation network for retinal vessel segmentation. In: 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020) (2020)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
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Xu, R., Liu, T., Ye, X., Lin, L., Chen, YW. (2020). Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_76
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