School of Optical-Electrical and Computer Engineering, University of Shanghai for Science andTechnology, Shanghai, 200093, China.
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science andTechnology, Shanghai, 200093, China.
Comput Biol Med. 2024 Dec;183:109352. doi: 10.1016/j.compbiomed.2024.109352. Epub 2024 Nov 5.
Accurate multi-lesion segmentation together with automated grading on fundus images played a vital role in diagnosing and treating diabetic retinopathy (DR). Nevertheless, the intrinsic patterns of fundus lesions aggravated challenges in DR detection process. Therefore, we proposed a novel multi-lesion segmentation guided deep attention network (MSGDA-Net) for accurate and automated DR detection, consisting of a DR lesion segmentation pathway as an auxiliary task to produce a lesion regional prior knowledge and a DR grading pathway to extract local fine-grained features and long-range dependency. In DR lesion segmentation pathway, we designed a Multi-Scale Attention Block (MSAB) and a Lesion-Aware Relation Block (LARB) to allow interactions among multi-lesion features for alleviating ambiguity in lesion segmentation, generating lesion regional prior knowledge. As for DR grading pathway, we presented a Spatial-Fusion Block (SFB) to enhance the lesion-related local fine-grained feature representations and eliminate irrelevant noise information under the guidance of the resulting lesion regional priors, while constructed an Enhanced Self-Attention Block (ESAB) to optimally fuse fine-grained features from SFB with long-range global-context information for grading DR. The experimental results showed that our MSGDA-Net not only achieved state-of-the-art performance in the tasks of multi-lesion segmentation and DR grading, reaching up to 49.21 % Dice, 38.05 % IoU and 51.15 % AUPR for DR lesion segmentation on the DDR dataset, as well as accuracy values of 75.00 % and 87.18 % for DR grading on local newly-built VisionDR and publicly available APTOS datasets, but also manifested good generalization and robustness on cross-data evaluation. It could serve as a promising tool for computer aided DR screening and diagnosis.
准确的多病灶分割以及对眼底图像的自动分级在诊断和治疗糖尿病视网膜病变（DR）方面发挥了至关重要的作用。然而，眼底病变的固有模式加剧了 DR 检测过程中的挑战。因此，我们提出了一种新的多病灶分割引导的深度注意网络（MSGDA-Net），用于准确和自动的 DR 检测，它由一个 DR 病变分割路径作为辅助任务，生成病变区域的先验知识，以及一个 DR 分级路径，用于提取局部细粒度特征和长程依赖关系。在 DR 病变分割路径中，我们设计了一个多尺度注意块（MSAB）和一个病变感知关系块（LARB），以允许多病变特征之间的相互作用，从而减轻病变分割中的歧义，生成病变区域的先验知识。对于 DR 分级路径，我们提出了一个空间融合块（SFB），以在病变区域先验知识的指导下增强病变相关的局部细粒度特征表示，并消除无关的噪声信息，同时构建了一个增强的自注意块（ESAB），以最优地融合来自 SFB 的细粒度特征与长程全局上下文信息，用于 DR 分级。实验结果表明，我们的 MSGDA-Net 不仅在多病灶分割和 DR 分级任务中达到了最新的性能，在 DDR 数据集上的 DR 病变分割任务中达到了 49.21%的 Dice 系数、38.05%的 IoU 系数和 51.15%的 AUPR 系数，在本地新建的 VisionDR 和公开可用的 APTOS 数据集上的 DR 分级任务中达到了 75.00%和 87.18%的准确率，而且在跨数据评估中表现出了良好的泛化和鲁棒性。它可以作为一种有前途的计算机辅助 DR 筛查和诊断工具。
