RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation
This work addresses retinal vessel segmentation for medical imaging, but it is incremental as it builds on existing CNN approaches with optimizations.
The authors tackled retinal vessel segmentation by proposing RC-Net, a fully convolutional network optimized to reduce complexity and feature overlapping, which outperformed alternative methods with significantly fewer trainable parameters.
Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchmark datasets. In this paper, we take a step back to examine the real need for such complexity. We present RC-Net, a fully convolutional network, where the number of filters per layer is optimized to reduce feature overlapping and complexity. We also used skip connections to keep spatial information loss to a minimum by keeping the number of pooling operations in the network to a minimum. Two publicly available retinal vessel segmentation datasets were used in our experiments. In our experiments, RC-Net is quite competitive, outperforming alternatives vessels segmentation methods with two or even three orders of magnitude less trainable parameters.