  Human-in-the-loop (HITL) strategy has been recently introduced into the field
of medical image processing. Indocyanine green angiography (ICGA) stands as a
well-established examination for visualizing choroidal vasculature and
detecting chorioretinal diseases. However, the intricate nature of choroidal
vascular networks makes large-scale manual segmentation of ICGA images
challenging. Thus, the study aims to develop a high-precision choroidal vessel
segmentation model with limited labor using HITL framework. We utilized a
multi-source ICGA dataset, including 55 degree view and ultra-widefield ICGA
(UWF-ICGA) images for model development. The choroidal vessel network was
pre-segmented by a pre-trained vessel segmentation model, and then manually
modified by two ophthalmologists. Choroidal vascular diameter, density,
complexity, tortuosity, and branching angle were automatically quantified based
on the segmentation. We finally conducted four cycles of HITL. One hundred and
fifty 55 degree view ICGA images were used for the first three cycles (50
images per cycle), and twenty UWF-ICGA images for the last cycle. The average
time needed to manually correct a pre-segmented ICGA image per cycle reduced
from 20 minutes to 1 minute. High segmentation accuracy has been achieved on
both 55 degree view ICGA and UWF-ICGA images. Additionally, the
multi-dimensional choroidal vascular parameters were significantly associated
with various chorioretinal diseases. Our study not only demonstrated the
feasibility of the HITL strategy in improving segmentation performance with
reduced manual labeling, but also innovatively introduced several risk
predictors for choroidal abnormalities.
