Purpose: To compare area measurements between swept source optical coherence tomography
angiography (SSOCTA), fluorescein angiography (FA), and indocyanine green angiography
(ICGA) after applying a novel deep-learning-assisted algorithm for accurate image
registration. Methods: We applied an algorithm for the segmentation of blood vessels
in FA, ICGA, and SSOCTA images of 24 eyes with treatment-naive neovascular age-related
macular degeneration. We trained a model based on U-Net and Mask R-CNN for each imaging
modality using vessel annotations and junctions to estimate scaling, translation,
and rotation. For fine-tuning of the registration, vessels and the elastix framework
were used. Area, perimeter, and circularity measurements were performed manually using
ImageJ. Results: Choroidal neovascularization lesion size, perimeter, and circularity
delineations showed no significant difference between SSOCTA and ICGA (allP> 0.05).
Choroidal neovascularization area showed excellent correlation between SSOCTA and
ICGA (r = 0.992) and a Bland-Altman bias of -0.10 +/- 0.24 mm(2). There was no significant
difference in foveal avascular zone size between SSOCTA and FA (P= 0.96) and an extremely
small bias of 0.0004 +/- 0.04 mm(2)and excellent correlation (r = 0.933). Foveal avascular
zone perimeter was not significantly different, but foveal avascular zone circularity
was significantly different (P= 0.047), indicating that some small cavities or gaps
may be missed leading to higher circularity values representing a more round-shaped
foveal avascular zone in FA. Conclusion: We found no statistically significant differences
between SSOCTA and FA and ICGA area measurements in patients with treatment-naive
neovascular age-related macular degeneration after applying a deep-learning-assisted
approach for image registration. These findings encourage a paradigm shift to using
SSOCTA as a first-line diagnostic tool in neovascular age-related macular degeneration.
