RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation

TL;DR

This paper introduces RTNet, a relation transformer network that leverages lesion-vessel relationships and attention mechanisms to improve multi-lesion segmentation in diabetic retinopathy, aiding ophthalmologists in diagnosis.

Contribution

The paper proposes a novel relation transformer block and a global transformer block to incorporate pathological associations and detailed lesion features for improved segmentation accuracy.

Findings

Achieves superior segmentation performance on IDRiD and DDR datasets.

Effectively models lesion-vessel relationships to reduce ambiguity.

Outperforms existing methods in multi-lesion segmentation accuracy.

Abstract

Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting ophthalmologists in diagnosis. Although many researches have been conducted on this task, most prior works paid too much attention to the designs of networks instead of considering the pathological association for lesions. Through investigating the pathogenic causes of DR lesions in advance, we found that certain lesions are closed to specific vessels and present relative patterns to each other. Motivated by the observation, we propose a relation transformer block (RTB) to incorporate attention mechanisms at two main levels: a self-attention transformer exploits global dependencies among lesion features, while a cross-attention transformer allows interactions between lesion and vessel features by integrating valuable vascular information to alleviate ambiguity in lesion detection caused by complex fundus structures. In addition, to capture the small lesion patterns first, we propose a global transformer block (GTB) which preserves detailed information in deep network. By integrating the above blocks of dual-branches, our network segments the four kinds of lesions simultaneously. Comprehensive experiments on IDRiD and DDR datasets well demonstrate the superiority of our approach, which achieves competitive performance compared to state-of-the-arts.