2pCePd-Net: Two-Path Cross-Context Encoder With Probability Map-Based Bandpass Decoder for Retinal Vessel Segmentation

Accurate automatic retinal blood vessel segmentation in fundus images plays an important role in the early diagnosis of any ocular disease detection system. However, most of the past literature has yet to attain a superior result primarily due to a lack of sufficient annotated data and the complexity of the vessel structure under challenging background conditions. In this work, we propose the design of a coherence measure-guided data augmentation model, named lambda-coherence measure-guided Cartesian-square ( $\lambda \text {CMgC}^{2}$ ), for enriching the existing datasets with synthetic and structurally coherent fundus images thus alleviating the issue of data scarcity. Subsequently, we propose a novel end-to-end convolutional network, called two-path cross-context encoder with probability map-based bandpass decoder (2pCePd-Net) for the segmentation of blood vessels endowed with a novel $\text {2pCd}^{+}$ encoder block with CERg skip connection and a novel $\hat {p}\text {BPf}$ -enabled decoder block. The proposed work has been evaluated using four standard datasets, namely, DRIVE, STARE, CHASEDB1, and HRF, and has obtained a benchmark accuracy (Ac) of 97.6%, 98.1%, 98.2%, and 97.7%, respectively. Statistically, our proposed model has further achieved benchmark results across sensitivity (Se), specificity (Sp), $F1$ , and AUC measures of evaluation as well.