  Optical Coherence Tomography Angiography (OCTA) has become increasingly vital
in the clinical screening of fundus diseases due to its ability to capture
accurate 3D imaging of blood vessels in a non-contact scanning manner. However,
the acquisition of OCTA images remains challenging due to the requirement of
exclusive sensors and expensive devices. In this paper, we propose a novel
framework, TransPro, that translates 3D Optical Coherence Tomography (OCT)
images into exclusive 3D OCTA images using an image translation pattern. Our
main objective is to address two issues in existing image translation
baselines, namely, the aimlessness in the translation process and
incompleteness of the translated object. The former refers to the overall
quality of the translated OCTA images being satisfactory, but the retinal
vascular quality being low. The latter refers to incomplete objects in
translated OCTA images due to the lack of global contexts. TransPro merges a 2D
retinal vascular segmentation model and a 2D OCTA image translation model into
a 3D image translation baseline for the 2D projection map projected by the
translated OCTA images. The 2D retinal vascular segmentation model can improve
attention to the retinal vascular, while the 2D OCTA image translation model
introduces beneficial heuristic contextual information. Extensive experimental
results on two challenging datasets demonstrate that TransPro can consistently
outperform existing approaches with minimal computational overhead during
training and none during testing.
