SAM-OCTA: A Fine-Tuning Strategy for Applying Foundation Model to OCTA
  Image Segmentation Tasks
Chengliang Wang Xinrun Chen Haojian Ning Shiying Li
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.