A Labeling-Free Approach to Supervising Deep Neural Networks for Retinal Blood Vessel Segmentation

Segmenting blood vessels in fundus imaging plays an important role in medical diagnosis. Many algorithms have been proposed. While deep Neural Networks have been attracting enormous attention from computer vision community recent years and several novel works have been done in terms of its application in retinal blood vessel segmentation, most of them are based on supervised learning which requires amount of labeled data, which is both scarce and expensive to obtain. We leverage the power of Deep Convolutional Neural Networks (DCNN) in feature learning, in this work, to achieve this ultimate goal. The highly efficient feature learning of DCNN inspires our novel approach that trains the networks with automatically-generated samples to achieve desirable performance on real-world fundus images. For this, we design a set of rules abstracted from the domain-specific prior knowledge to generate these samples. We argue that, with the high efficiency of DCNN in feature learning, one can achieve this goal by constructing the training dataset with prior knowledge, no manual labeling is needed. This approach allows us to take advantages of supervised learning without labeling. We also build a naive DCNN model to test it. The results on standard benchmarks of fundus imaging show it is competitive to the state-of-the-art methods which implies a potential way to leverage the power of DCNN in feature learning.