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.

翻译：在光学相干断层扫描血管成像（OCTA）图像分析中，需要对特定目标进行分割操作。现有方法通常基于样本量有限（约数百张）的监督数据集进行训练，这可能导致过拟合问题。为此，本文采用低秩适应技术对基础模型进行微调，并设计相应的提示点生成策略，以处理OCTA数据集中的多种分割任务。该方法被命名为SAM-OCTA，并在公开的OCTA-500数据集上进行了实验。在取得最先进性能指标的同时，该方法实现了局部血管分割以及有效的动静脉分割，而这一问题在先前的工作中未能得到良好解决。代码已在 https://github.com/ShellRedia/SAM-OCTA 中开源。