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In the analysis of optical coherence tomography angiography (OCTA) images,the operation of segmenting specific targets is necessary. Existing methodstypically train on supervised datasets with limited samples (approximately afew hundred), which can lead to overfitting. To address this, the low-rankadaptation technique is adopted for foundation model fine-tuning and proposedcorresponding prompt point generation strategies to process varioussegmentation tasks on OCTA datasets. This method is named SAM-OCTA and has beenexperimented on the publicly available OCTA-500 dataset. While achievingstate-of-the-art performance metrics, this method accomplishes local vesselsegmentation as well as effective artery-vein segmentation, which was notwell-solved in previous works. The code is available at:https://github.com/ShellRedia/SAM-OCTA.

SAM-OCTA: A Fine-Tuning Strategy for Applying Foundation Model to OCTA Image Segmentation Tasks