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

• URL: http://arxiv.org/abs/2309.11758v1
• Date: Thu, 21 Sep 2023 03:41:08 GMT
• Title: SAM-OCTA: A Fine-Tuning Strategy for Applying Foundation Model to OCTA Image Segmentation Tasks
• Authors: Chengliang Wang, Xinrun Chen, Haojian Ning, Shiying Li
• Abstract summary: Low-rank adaptation technique is adopted for foundation model fine-tuning and proposed corresponding prompt point generation strategies. 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.
• Score: 2.8743451550676866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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