Residual Connection-Based Encoder Decoder Network (RCED-Net) for Retinal Vessel Segmentation

Abstract

Devising automated procedures for accurate vessel segmentation (retinal) is crucial for timely prognosis of vision-threatening eye diseases. In this paper, a novel supervised deep learning-based approach is proposed which extends a variant of the fully convolutional neural network. The existing fully convolutional neural network-based counterparts have associated critical drawbacks of involving a large number of tunable hyper-parameters and an increased end-to-end training time furnished by their decoder structure. The proposed approach addresses these intricate challenges by using a skip-connections strategy by sharing indices obtained through max-pooling to the decoder from the encoder stage (respective stages) for enhancing the resolution of the feature map. This significantly reduces the number of required tunable hyper-parameters and the computational overhead of the training as well as testing stages. Furthermore, the proposed approach particularly helps in eradicating the requirement for employing both post-processing and pre-processing steps. In the proposed approach, the retinal vessel segmentation problem is formulated as a semantic pixel-wise segmentation task which helps in spanning the gap between semantic segmentation and medical image segmentation. A prime contribution of the proposed approach is the introduction of external skip-connection for passing the preserved low-level semantic edge information in order to reliably detect tiny vessels in the retinal fundus images. The performance of the proposed scheme is analyzed based on the three publicly available notable fundus image datasets, while the widely recognized evaluation metrics of specificity, sensitivity, accuracy, and the Receiver Operating Characteristics curves are used. Based on the assessment of the images in {DRIVE, CHASE_DB1, and STARE}; datasets, the proposed approach achieves a sensitivity, specificity, accuracy, and ROC performance of {0.8252, 0.8440, and 0.8397};, {0.9787, 0.9810, and 0.9792};, {0.9649, 0.9722, and 0.9659};, and {0.9780, 0.9830, and 0.9810};, respectively. The reduced computational complexity and memory overhead along with improved segmentation performance advocates employing the proposed approach in the automated diagnostic systems for eye diseases.

Keywords

• Retinal vessel segmentation
• convolutional neural network
• pixel-wise semantic segmentation
• skip connection
• low-level semantic information
• residual edge information