  Retinal vessel segmentation is a crucial step in diagnosing and screening
various diseases, including diabetes, ophthalmologic diseases, and
cardiovascular diseases. In this paper, we propose an effective and efficient
method for vessel segmentation in color fundus images using encoder-decoder
based octave convolution networks. Compared with other convolution networks
utilizing standard convolution for feature extraction, the proposed method
utilizes octave convolutions and octave transposed convolutions for learning
multiple-spatial-frequency features, thus can better capture retinal
vasculatures with varying sizes and shapes. To provide the network the
capability of learning how to decode multifrequency features, we extend octave
convolution and propose a new operation named octave transposed convolution. A
novel architecture of convolutional neural network, named as Octave UNet
integrating both octave convolutions and octave transposed convolutions is
proposed based on the encoder-decoder architecture of UNet, which can generate
high resolution vessel segmentation in one single forward feeding without
post-processing steps. Comprehensive experimental results demonstrate that the
proposed Octave UNet outperforms the baseline UNet achieving better or
comparable performance to the state-of-the-art methods with fast processing
speed. Specifically, the proposed method achieves 0.9664 / 0.9713 / 0.9759 /
0.9698 accuracy, 0.8374 / 0.8664 / 0.8670 / 0.8076 sensitivity, 0.9790 / 0.9798
/ 0.9840 / 0.9831 specificity, 0.8127 / 0.8191 / 0.8313 / 0.7963 F1 score, and
0.9835 / 0.9875 / 0.9905 / 0.9845 Area Under Receiver Operating Characteristic
curve, on DRIVE, STARE, CHASE_DB1, and HRF datasets, respectively.
