arXiv 2503.059912025-03-08eess.IVcs.AIcs.CVcs.LG

GrInAdapt：通过接地、整合与适应多设备、多站点和多模态眼底域来扩展视网膜血管结构图分割

GrInAdapt: Scaling Retinal Vessel Structural Map Segmentation Through Grounding, Integrating and Adapting Multi-device, Multi-site, and Multi-modal Fundus Domains

Department of Bioengineering, University of Washington（华盛顿大学生物工程系）；Department of Electrical and Computer Engineering, University of Washington（华盛顿大学电气与计算机工程系）；Department of Ophthalmology, University of Washington（华盛顿大学眼科系）

Zixuan Liu, Aaron Honjaya, Yuekai Xu, Yi Zhang, Hefu Pan, Xin Wang, Linda G Shapiro, Sheng Wang, Ruikang K Wang

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AI总结：

GrInAdapt 提出一种无源多目标域适应框架，通过配准接地、多视图预测整合和源模型适应三步，并融合彩色眼底照相，显著提升多设备、多站点、多模态 OCTA 视网膜血管分割精度与鲁棒性。

摘要：

视网膜血管分割对于诊断眼部疾病至关重要，然而当前的深度学习方法受到模态特定挑战以及成像设备、分辨率和解剖区域之间显著分布偏移的限制。在本文中，我们提出了 GrInAdapt，这是一种用于无源多目标域适应的新型框架，利用多视图图像来细化分割标签，并增强模型对眼部眼底光学相干断层扫描血管造影（OCTA）的泛化能力。GrInAdapt 遵循直观的三步方法：（i）通过配准将图像接地到公共锚定空间，（ii）整合来自多个视图的预测以实现改进的标签一致性，以及（iii）将源模型适应到多样的目标域。此外，GrInAdapt 足够灵活，可以纳入辅助模态（如彩色眼底照相），为稳健的血管分割提供互补线索。在多设备、多站点和多模态视网膜数据集上的大量实验表明，GrInAdapt 显著优于现有的域适应方法，在多个域上实现了更高的分割精度和鲁棒性。这些结果凸显了 GrInAdapt 在推进自动化视网膜血管分析和支持稳健临床决策方面的潜力。

英文摘要：

Retinal vessel segmentation is critical for diagnosing ocular conditions, yet current deep learning methods are limited by modality-specific challenges and significant distribution shifts across imaging devices, resolutions, and anatomical regions. In this paper, we propose GrInAdapt, a novel framework for source-free multi-target domain adaptation that leverages multi-view images to refine segmentation labels and enhance model generalizability for optical coherence tomography angiography (OCTA) of the fundus of the eye. GrInAdapt follows an intuitive three-step approach: (i) grounding images to a common anchor space via registration, (ii) integrating predictions from multiple views to achieve improved label consensus, and (iii) adapting the source model to diverse target domains. Furthermore, GrInAdapt is flexible enough to incorporate auxiliary modalities such as color fundus photography, to provide complementary cues for robust vessel segmentation. Extensive experiments on a multi-device, multi-site, and multi-modal retinal dataset demonstrate that GrInAdapt significantly outperforms existing domain adaptation methods, achieving higher segmentation accuracy and robustness across multiple domains. These results highlight the potential of GrInAdapt to advance automated retinal vessel analysis and support robust clinical decision-making.

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