MEMO：面向存在大或小血管密度差异的鲁棒多模态视网膜图像配准的数据集与方法
MEMO: Dataset and Methods for Robust Multimodal Retinal Image Registration with Large or Small Vessel Density Differences
AI总结：
针对EMA与OCTA两种模态视网膜图像配准受血管密度差异影响的问题，构建了首个公开多模态数据集MEMO，提出VDD-Reg配准框架和MSD指标，仅需少量标注即可在不同血管密度差异场景下取得优于基线的效果。
摘要：
视网膜毛细血管中的视网膜血流（RBF）测量可为眼部疾病的早期诊断与治疗提供强有力的生物标志物。然而，单一模态无法高精度测定毛细血管流速。将红细胞介导血管造影术（erythrocyte-mediated angiography, EMA）与光学相干断层扫描血管造影术（optical coherence tomography angiography, OCTA）相结合有望实现这一目标，因为EMA可测量视网膜微血管的绝对二维RBF，而OCTA可提供毛细血管的三维结构图像。但这两种模态之间的多模态视网膜图像配准研究仍十分欠缺。为填补这一空白，我们构建了首个公开的EMA与OCTA多模态视网膜图像数据集MEMO。这两种模态的多模态视网膜图像配准面临的一个独特挑战是血管密度（vessel density, VD）存在较大差异。为应对这一挑战，我们提出了一种基于分割的深度学习框架VDD-Reg和一种新的评估指标MSD，二者在存在血管密度差异的情况下仍能输出鲁棒的结果。VDD-Reg由血管分割模块和配准模块组成。为训练血管分割模块，我们进一步设计了一种结合监督损失与无监督损失的两阶段半监督学习框架LVD-Seg。我们通过实验证明，在血管密度差异较小（使用CF-FA数据集）和较大（使用我们的MEMO数据集）的场景下，VDD-Reg在定量和定性表现上均优于基线方法。此外，VDD-Reg仅需3个带标注的血管分割掩码即可维持其精度，证明了其可行性。
英文摘要：
The measurement of retinal blood flow (RBF) in capillaries can provide a powerful biomarker for the early diagnosis and treatment of ocular diseases. However, no single modality can determine capillary flowrates with high precision. Combining erythrocyte-mediated angiography (EMA) with optical coherence tomography angiography (OCTA) has the potential to achieve this goal, as EMA can measure the absolute 2D RBF of retinal microvasculature and OCTA can provide the 3D structural images of capillaries. However, multimodal retinal image registration between these two modalities remains largely unexplored. To fill this gap, we establish MEMO, the first public multimodal EMA and OCTA retinal image dataset. A unique challenge in multimodal retinal image registration between these modalities is the relatively large difference in vessel density (VD). To address this challenge, we propose a segmentation-based deep-learning framework (VDD-Reg) and a new evaluation metric (MSD), which provide robust results despite differences in vessel density. VDD-Reg consists of a vessel segmentation module and a registration module. To train the vessel segmentation module, we further designed a two-stage semi-supervised learning framework (LVD-Seg) combining supervised and unsupervised losses. We demonstrate that VDD-Reg outperforms baseline methods quantitatively and qualitatively for cases of both small VD differences (using the CF-FA dataset) and large VD differences (using our MEMO dataset). Moreover, VDD-Reg requires as few as three annotated vessel segmentation masks to maintain its accuracy, demonstrating its feasibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
