  Fundus photography and Optical Coherence Tomography Angiography (OCT-A) are
two commonly used modalities in ophthalmic imaging. With the development of
deep learning algorithms, fundus image processing, especially retinal vessel
segmentation, has been extensively studied. Built upon the known operator
theory, interpretable deep network pipelines with well-defined modules have
been constructed on fundus images. In this work, we firstly train a modularized
network pipeline for the task of retinal vessel segmentation on the fundus
database DRIVE. The pretrained preprocessing module from the pipeline is then
directly transferred onto OCT-A data for image quality enhancement without
further fine-tuning. Output images show that the preprocessing net can balance
the contrast, suppress noise and thereby produce vessel trees with improved
connectivity in both image modalities. The visual impression is confirmed by an
observer study with five OCT-A experts. Statistics of the grades by the experts
indicate that the transferred module improves both the image quality and the
diagnostic quality. Our work provides an example that modules within network
pipelines that are built upon the known operator theory facilitate
cross-modality reuse without additional training or transfer learning.
