Polygon-mamba: 使用多边形扫描Mamba和空间-频率协同注意力进行视网膜血管分割
Polygon-mamba: Retinal vessel segmentation using polygon scanning mamba and space-frequency collaborative attention
AI总结：
针对视网膜小血管分割难题，提出一种混合CNN-Mamba融合网络，通过多边形扫描Mamba和空间-频率协同注意力机制，有效保留像素连通性并增强关键特征，在三个公开数据集上取得优异性能。
摘要：
视网膜血管分割对于眼部疾病的诊断和评估至关重要。值得注意的是，小血管的分割一直被认为是一项具有挑战性和复杂性的任务。为了应对这一挑战，我们设计了一种混合CNN-Mamba融合网络，该网络集成了多边形扫描Mamba和空间-频率协同注意力机制，用于检测小血管。考虑到传统的水平-垂直扫描Mamba架构可能会破坏目标结构的拓扑完整性，并导致视网膜小血管的局部不连续性，我们提出了一种多边形扫描视觉状态空间模型（PS-VSS），通过多层反向扫描方式识别小血管结构特征。该方法有效保留了像素连通性，从而显著减轻了小血管信息的丢失。此外，众所周知，空间域优先考虑位置和结构信息，而频率域强调全局感知和局部细节成分，我们在跳跃连接中引入了空间-频率协同注意力机制（SFCAM），以从空间域和频率域提取高效特征。该策略使模型能够动态增强关键特征，同时有效抑制杂乱信息。为了评估模型的有效性，我们在三个公开数据集：DRIVE、STARE和CHASE_DB1上进行了测试。与手动标注相比，我们的模型在三个数据集上的F1分数分别为0.8283、0.8282和0.8251，曲线下面积（AUC）值分别为0.9806、0.9840和0.9866，灵敏度（SE）值分别为0.8268、0.8314和0.8484。通过视觉检查和定量分析验证了模型的有效性。
英文摘要：
Retinal vessel segmentation is crucial for diagnosis and assessment of ocular diseases. Notably, segmentation of small retinal vessels has been consistently recognized as a challenging and complex task. To tackle this challenge, we design a hybrid CNN-Mamba fusion network that integrates polygon scanning mamba and space-frequency collaborative attention mechanism for the detection of small vessels. Considering that the traditional mamba architecture with horizontal-vertical scanning may compromise the topological integrity of target structures and result in local discontinuities in small retinal vessels, we present a polygon scanning visual state space model (PS-VSS) to identify small vessel structural features by multi-layer reverse scanning way. Which effectively preserves pixels connectivity, thereby substantially mitigating the loss of information pertaining to small vessels. Furthermore, as we all known that the spatial domain prioritizes positional and structural information, while the frequency domain emphasizes global perception and local detail components, a space-frequency collaborative attention mechanism (SFCAM) is introduced within the skip connection to extract efficient features from the spatial and frequency domains. This strategy empowers the model to dynamically enhance the key features while effectively suppressing clutters. To assess the efficacy of our model, it was tested on three publicly available datasets: DRIVE, STARE, and CHASE_DB1. Compared to manual annotations, our model demonstrated F1 scores of 0.8283, 0.8282, and 0.8251, Area Under Curve (AUC) values of 0.9806, 0.9840, and 0.9866, and Sensitivity (SE) values of of 0.8268, 0.8314, and 0.8484 across three datasets, respectively. The effectiveness of our model was validated through both visual inspection and quantitative analysis.
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