IEEE Trans Med Imaging. 2022 Jun;41(6):1596-1607. doi: 10.1109/TMI.2022.3143833. Epub 2022 Jun 1.
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.
自动糖尿病性视网膜病变（DR）病变分割对于辅助眼科医生诊断具有重要意义。尽管已经对这一任务进行了许多研究，但大多数先前的工作过于注重网络的设计，而没有考虑病变的病理关联。通过提前研究 DR 病变的发病原因，我们发现某些病变与特定血管密切相关，彼此之间呈现出相对的模式。受此观察的启发，我们提出了一种关系转换器块（RTB），在两个主要层面上引入注意力机制：自注意力转换器利用病变特征之间的全局依赖关系，而交叉注意力转换器通过整合有价值的血管信息允许病变和血管特征之间的相互作用，从而减轻由于眼底结构复杂而导致的病变检测中的歧义。此外，为了首先捕获小病变模式，我们提出了一个全局转换器块（GTB），它保留了深层网络中的详细信息。通过整合双分支的上述块，我们的网络可以同时分割四种病变。在 IDRiD 和 DDR 数据集上的综合实验很好地证明了我们方法的优越性，与最先进的方法相比，我们的方法具有竞争力。
