  Segmenting specific targets or biomarkers is necessary to analyze optical
coherence tomography angiography (OCTA) images. Previous methods typically
segment all the targets in an OCTA sample, such as retinal vessels (RVs).
Although these methods perform well in accuracy and precision, OCTA analyses
often focusing local information within the images which has not been
fulfilled. In this paper, we propose a method called SAM-OCTA for local
segmentation in OCTA images. The method fine-tunes a pre-trained segment
anything model (SAM) using low-rank adaptation (LoRA) and utilizes prompt
points for local RVs, arteries, and veins segmentation in OCTA. To explore the
effect and mechanism of prompt points, we set up global and local segmentation
modes with two prompt point generation strategies, namely random selection and
special annotation. Considering practical usage, we conducted extended
experiments with different model scales and analyzed the model performance
before and after fine-tuning besides the general segmentation task. From
comprehensive experimental results with the OCTA-500 dataset, our SAM-OCTA
method has achieved state-of-the-art performance in common OCTA segmentation
tasks related to RV and FAZ, and it also performs accurate segmentation of
artery-vein and local vessels. The code is available at
https://github.com/ShellRedia/SAM-OCTA-extend.
