DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field

DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field
Title:
DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field
Other Titles:
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
DOI:
10.1007/978-3-319-46723-8_16
Keywords:
Publication Date:
02 October 2016
Citation:
Abstract:
Retinal vessel segmentation is a fundamental step for various ocular imaging applications. In this paper, we formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture. Our method is based on two key ideas: (1) applying a multi-scale and multi-level Convolutional Neural Network (CNN) with a side-output layer to learn a rich hierarchical representation, and (2) utilizing a Conditional Random Field (CRF) to model the long-range interactions between pixels. We combine the CNN and CRF layers into an integrated deep network called DeepVessel. Our experiments show that the DeepVessel system achieves state-of-the-art retinal vessel segmentation performance on the DRIVE, STARE, and CHASE_DB1 datasets with an efficient running time.
License type:
PublisherCopyrights
Funding Info:
Description:
ISSN:
978-3-319-46723-8
ISBN:
978-3-319-46722-1
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