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