Retinal vessel segmentation via deep learning network and fully-connected conditional random fields

Retinal vessel segmentation via deep learning network and fully-connected conditional random fields
Title:
Retinal vessel segmentation via deep learning network and fully-connected conditional random fields
Other Titles:
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
DOI:
10.1109/ ISBI.2016.7493362
Keywords:
Publication Date:
13 April 2016
Citation:
H. Fu, Y. Xu, D. W. K. Wong and J. Liu, "Retinal vessel segmentation via deep learning network and fully-connected conditional random fields," 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, 2016, pp. 698-701. doi: 10.1109/ ISBI.2016.7493362
Abstract:
Vessel segmentation is a key step for various medical applications. This paper introduces the deep learning architecture to improve the performance of retinal vessel segmentation. Deep learning architecture has been demonstrated having the powerful ability in automatically learning the rich hierarchical representations. In this paper, we formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map. Our vessel probability map distinguishes the vessels and background in the inadequate contrast region, and has robustness to the pathological regions in the fundus image. Moreover, a fully-connected Conditional Random Fields (CRFs) is also employed to combine the discriminative vessel probability map and long-range interactions between pixels. Finally, a binary vessel segmentation result is obtained by our method. We show that our proposed method achieve a state-of-the-art vessel segmentation performance on the DRIVE and STARE datasets.
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(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
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