Abstract
The research of retinal vessel segmentation prevails since retinal vessels well indicate the diseases, such as diabetic retinopathy, glaucoma and hypertension. This paper proposes an efficient CNN-CRF framework to segment the vessels from digital retinal images. Our approach combines the prediction ability of CNN and the segmentation ability of CRF, and trains an end-to-end deep learning segmentation model for retinal images. Unlike pixel-wise segmentation, our network is able to segment one image during once network forward computation. When applying our CNN-CRF to the DRIVE database, the average accuracy achieves 0.9536 with the average recall rate of 0.7508, outperforming the state-of-art approaches. And our approach requires only 0.53 s per image, the fastest among deep learning approaches.
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Acknowledgment
The authors would like to thank all the reviewers for their insightful comments. This work was supported by the National Natural Science Foundation of China (Grant No. 61305033, 61273256 and 6157021026), Fundamental Research Funds for the Central Universities (ZYGX2014Z009).
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Luo, Y., Yang, L., Wang, L., Cheng, H. (2017). Efficient CNN-CRF Network for Retinal Image Segmentation. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_17
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DOI: https://doi.org/10.1007/978-981-10-5230-9_17
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