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The measurement of retinal blood flow (RBF) in capillaries can provide apowerful biomarker for the early diagnosis and treatment of ocular diseases.However, no single modality can determine capillary flowrates with highprecision. Combining erythrocyte-mediated angiography (EMA) with opticalcoherence tomography angiography (OCTA) has the potential to achieve this goal,as EMA can measure the absolute 2D RBF of retinal microvasculature and OCTA canprovide the 3D structural images of capillaries. However, multimodal retinalimage registration between these two modalities remains largely unexplored. Tofill this gap, we establish MEMO, the first public multimodal EMA and OCTAretinal image dataset. A unique challenge in multimodal retinal imageregistration between these modalities is the relatively large difference invessel density (VD). To address this challenge, we propose a segmentation-baseddeep-learning framework (VDD-Reg) and a new evaluation metric (MSD), whichprovide robust results despite differences in vessel density. VDD-Reg consistsof a vessel segmentation module and a registration module. To train the vesselsegmentation module, we further designed a two-stage semi-supervised learningframework (LVD-Seg) combining supervised and unsupervised losses. Wedemonstrate that VDD-Reg outperforms baseline methods quantitatively andqualitatively for cases of both small VD differences (using the CF-FA dataset)and large VD differences (using our MEMO dataset). Moreover, VDD-Reg requiresas few as three annotated vessel segmentation masks to maintain its accuracy,demonstrating its feasibility.

MEMO: Dataset and Methods for Robust Multimodal Retinal Image Registration with Large or Small Vessel Density Differences