Shallow Vessel Segmentation Network for Automatic Retinal Vessel Segmentation

Abstract

Accurate automatic segmentation of the retinal vessels is crucial for early detection and diagnosis of vision threatening retinal diseases. This paper presents a lightweight convolutional neural network termed as Shallow Vessel Segmentation Network (SVSN) for vessel segmentation. To achieve semantic segmentation encoder-decoder structures embedded with spatial pyramid pooling modules are used. After checking the input features with pooling through multiple fields of view and rates, it becomes easy for the erstwhile networks to encode multi-scale contextual information. While boundaries for sharper objects are captured by the prevalent networks. Moreover, the need for pre- and post-processing steps are eradicated. Consequently, the detection accuracy is significantly improved with scores of 0.9625 and 0.9645 on DRIVE and STARE datasets respectively.