专为眼底图像优化，提升视盘与视杯分割精度
FunduSAM: A Specialized Deep Learning Model for Enhanced Optic Disc and Cup Segmentation in Fundus Images
- 在SAM中嵌入适配器与注意力模块，增强特征提取能力
- 在REFUGE数据集上超越5种主流方法，分割更精准
- 适合眼科医学图像分析研究者参考使用
分割一切模型（SAM）因其强大的跨领域泛化能力而广受欢迎。但在视盘（OD）和视杯（OC）分割任务中，由于眼底图像结构复杂、对比度低、边界模糊，SAM表现不佳。为此，我们提出FunduSAM，通过在SAM每个编码器后的Transformer块中引入适配器（Adapter），实现参数高效微调（PEFT），并设计卷积块注意力模块（CBAM）以改善模糊边界和低对比度问题。针对OD与OC分割的特殊需求，采用极坐标变换将原始眼底图像转换为更适合训练与评估的格式。使用联合损失函数，兼顾结构一致性与分割准确性。在包含1,200张眼底图像的REFUGE数据集上，实验表明FunduSAM显著优于五种主流方法。
原文摘要 · Abstract (English)
The Segment Anything Model (SAM) has gained popularity as a versatile image segmentation method, thanks to its strong generalization capabilities across various domains. However, when applied to optic disc (OD) and optic cup (OC) segmentation tasks, SAM encounters challenges due to the complex structures, low contrast, and blurred boundaries typical of fundus images, leading to suboptimal performance. To overcome these challenges, we introduce a novel model, FunduSAM, which incorporates several Adapters into SAM to create a deep network specifically designed for OD and OC segmentation. The FunduSAM utilizes Adapter into each transformer block after encoder for parameter fine-tuning (PEFT). It enhances SAM's feature extraction capabilities by designing a Convolutional Block Attention Module (CBAM), addressing issues related to blurred boundaries and low contrast. Given the unique requirements of OD and OC segmentation, polar transformation is used to convert the original fundus OD images into a format better suited for training and evaluating FunduSAM. A joint loss is used to achieve structure preservation between the OD and OC, while accurate segmentation. Extensive experiments on the REFUGE dataset, comprising 1,200 fundus images, demonstrate the superior performance of FunduSAM compared to five mainstream approaches.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。