用微调SAM2实现OCTA层序列血管精准分割与追踪
SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2
- 用低秩适配微调SAM2，支持跨层序列目标追踪
- 在OCTA-500上实现视网膜中央无血管区分割新纪录
- 仅需稀疏标注即可生成多层血管掩码，适合医学影像研究者
目标分割有助于精确分析光学相干断层扫描血管成像（OCTA）样本。现有方法通常针对2D投影图像进行分割，难以捕捉3D体积中目标的变异。为解决此问题，本文采用低秩适配技术微调Segment Anything Model（SAM）版本2，实现对OCTA扫描层序列中指定对象的追踪与分割。为进一步提升性能，提出帧序列提示点生成策略和稀疏标注方法，用于获取视网膜血管（RV）层掩码。该方法命名为SAM-OCTA2，已在OCTA-500数据集上验证，实现了常规2D横断面视网膜中央无血管区（FAZ）分割的最先进水平，并有效追踪了扫描层序列中的局部血管。代码已公开于https://github.com/ShellRedia/SAM-OCTA2。
原文摘要 · Abstract (English)
Segmentation of indicated targets aids in the precise analysis of optical coherence tomography angiography (OCTA) samples. Existing segmentation methods typically perform on 2D projection targets, making it challenging to capture the variance of segmented objects through the 3D volume. To address this limitation, the low-rank adaptation technique is adopted to fine-tune the Segment Anything Model (SAM) version 2, enabling the tracking and segmentation of specified objects across the OCTA scanning layer sequence. To further this work, a prompt point generation strategy in frame sequence and a sparse annotation method to acquire retinal vessel (RV) layer masks are proposed. This method is named SAM-OCTA2 and has been experimented on the OCTA-500 dataset. It achieves state-of-the-art performance in segmenting the foveal avascular zone (FAZ) on regular 2D en-face and effectively tracks local vessels across scanning layer sequences. The code is available at: https://github.com/ShellRedia/SAM-OCTA2.
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