Dual encoding feature filtering generalized attention UNET for retinal vessel segmentation

TL;DR

This paper introduces DEFFA-Unet, a novel retinal vessel segmentation model that enhances feature extraction and generalization through dual encoding, feature filtering, attention-guided fusion, and advanced data augmentation, outperforming existing methods.

Contribution

The paper proposes DEFFA-Unet with a dual encoder, feature filtering fusion, and attention-guided modules, addressing data scarcity and imbalance to improve segmentation accuracy and model generalization.

Findings

Outperforms baseline and state-of-the-art models on multiple datasets.

Demonstrates superior cross-validation generalization.

Effective handling of data imbalance and scarcity.

Abstract

Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases. Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field, yet issues like limited training data, imbalance data distribution, and inadequate feature extraction persist, hindering both the segmentation performance and optimal model generalization. Addressing these critical issues, the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs, thereby improving both richer feature encoding and enhanced model generalization. A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion. In response to the task-specific need for higher precision where false positives are very costly, traditional skip connections are replaced with the attention-guided feature reconstructing fusion module. Additionally, innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance, further boosting the robustness and generalization of the model. With a comprehensive suite of evaluation metrics, extensive validations on four benchmark datasets (DRIVE, CHASEDB1, STARE, and HRF) and an SLO dataset (IOSTAR), demonstrate the proposed method's superiority over both baseline and state-of-the-art models. Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.