Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels
This provides a reliable and fully automated method for diagnosing and monitoring diseases like diabetes and hypertension by accurately segmenting retinal vessels, with incremental improvements in efficiency and speed.
The authors tackled the problem of automatic segmentation of retinal vessels by proposing a novel fully convolutional deep neural network with dilated spatial pyramid pooling, achieving state-of-the-art results on the DRIVE and CHASE-DB1 datasets.
The reliable segmentation of retinal vasculature can provide the means to diagnose and monitor the progression of a variety of diseases affecting the blood vessel network, including diabetes and hypertension. We leverage the power of convolutional neural networks to devise a reliable and fully automated method that can accurately detect, segment, and analyze retinal vessels. In particular, we propose a novel, fully convolutional deep neural network with an encoder-decoder architecture that employs dilated spatial pyramid pooling with multiple dilation rates to recover the lost content in the encoder and add multiscale contextual information to the decoder. We also propose a simple yet effective way of quantifying and tracking the widths of retinal vessels through direct use of the segmentation predictions. Unlike previous deep-learning-based approaches to retinal vessel segmentation that mainly rely on patch-wise analysis, our proposed method leverages a whole-image approach during training and inference, resulting in more efficient training and faster inference through the access of global content in the image. We have tested our method on two publicly available datasets, and our state-of-the-art results on both the DRIVE and CHASE-DB1 datasets attest to the effectiveness of our approach.