基于多尺度高阶注意力机制的视网膜血管分割

视网膜血管分割对于辅助医生诊断糖尿病性视网膜病变、黄斑萎缩、青光眼等眼科疾病具有重要意义. 注意力机制被广泛用于U-Net及其变体中以提高血管分割模型的性能. 为进一步提高视网膜血管的分割精度, 挖掘视网膜图像中的高阶及全局上下文信息, 本文提出基于多尺度高阶注意力机制的模型(multi-scale high-order attention network, MHA-Net). 首先, 多尺度高阶注意力(multi-scale high-order attention, MHA)模块从深层特征图中提取多尺度和全局特征计算初始化注意力图, 从而改进模型处理医学图像分割时尺度不变的缺陷. 接下来, 该模块通过图的传递闭包构建注意力图, 进而提取高阶的深层特征. 通过将多尺度高阶注意力模块应用于编码器-解码器结构中, 在彩色眼底图像数据集DRIVE上进行血管分割, 实验结果表明, 基于多尺度高阶注意力机制的视网膜血管分割方法有效地提高了分割的精度.

Retinal Vessel Segmentation Based on Multi-scale High-order Attention Mechanism

Retinal vessel segmentation is vital for assisting doctors in diagnosing ophthalmic diseases, including diabetic retinopathy, macular atrophy, and glaucoma. The attention mechanism is widely used in U-Net and its variants to improve the vessel segmentation performance. For more accurate retinal vessel segmentation and exploration of high-order and global context information, we propose a multi-scale high-order attention network (MHA-Net). The multi-scale high-order attention (MHA) module first extracts multi-scale and global features from the high-level feature maps to compute the initial attention map, enabling the model to handle medical image segmentation with variable scales. Then the high-order attention constructs the attention map through graph transduction followed by the extraction of high-level features at high order. We further embed the proposed MHA module into an efficient encoder-decoder structure for retinal vessel segmentation. Comprehensive experiments are conducted on the color fundus image dataset DRIVE, which indicates that the proposed method improves the accuracy of retinal vessel segmentation effectively.