ResWnet for Retinal Small Vessel Segmentation

Abstract

U-Net shows excellent performance in biomedical image segmentation tasks, Variants of U-Net have been proposed, such as U-Net with residual blocks, densely connected convolution, or deformed convolution. However, contracting path of these variants is containing four consecutive downsampling layers and an encoding-decoding structure with skip connections. The feature map transformation paths are limited. Therefore, ResWnet is proposed in this work, the number of downsampling layers of contracting path is modified to two, and the network is changed from the encoding-decoding structure to the encoding-decoding-encoding-decoding structure so that the network can retain more detailed features and extract deeper semantic information. The skip connections are used between the contracting path and the expansion path at the same scale to increase the information transmission path. The convergence speed of the network is accelerated by the residual block replacing convolution layer. The image is scaled to different scales and is inputted into the network from the left to enhance the sensitivity of the network for blood vessels at different scales. The feature maps extracted from different layers of the network are scaled to the same size as the inputted image for fusion and segmentation. The performance of ResWnet is evaluated on DRIVE and STARE databases. The AUC is 0.9799 and 0.9863, and the accuracy is 0.9554 and 0.9723, respectively. According to the results, ResWnet outperforms many other proposed methods in small vessel segmentation. For future research, it can be learned that pre-processing and post-processing are vital to the segmentation accuracy. Furthermore, ResWnet can also be applied to other semantic segmentation tasks such as lung nodule segmentation or cancer cell segmentation. For future research, the pre-processing and post-processing will be tried to further enhance the segmentation accuracy performance of ResWnet.