  In the analysis of optical coherence tomography angiography (OCTA) images,
the operation of segmenting specific targets is necessary. Existing methods
typically train on supervised datasets with limited samples (approximately a
few hundred), which can lead to overfitting. To address this, the low-rank
adaptation technique is adopted for foundation model fine-tuning and proposed
corresponding prompt point generation strategies to process various
segmentation tasks on OCTA datasets. This method is named SAM-OCTA and has been
experimented on the publicly available OCTA-500 dataset. While achieving
state-of-the-art performance metrics, this method accomplishes local vessel
segmentation as well as effective artery-vein segmentation, which was not
well-solved in previous works. The code is available at:
https://github.com/ShellRedia/SAM-OCTA.
