School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210000, China.
Med Phys. 2021 Jul;48(7):3827-3841. doi: 10.1002/mp.14944. Epub 2021 Jun 16.
The segmentation results of retinal blood vessels have a significant impact on the automatic diagnosis of various ophthalmic diseases. In order to further improve the segmentation accuracy of retinal vessels, we propose an improved algorithm based on multiscale vessel detection, which extracts features through densely connected networks and reuses features.
A parallel fusion and serial embedding multiscale feature dense connection U-Net structure are designed. In the parallel fusion method, features of the input images are extracted for Inception multiscale convolution and dense block convolution, respectively, and then the features are fused and input into the subsequent network. In serial embedding mode, the Inception multiscale convolution structure is embedded in the dense connection network module, and then the dense connection structure is used to replace the classical convolution block in the U-Net network encoder part, so as to achieve multiscale feature extraction and efficient utilization of complex structure vessels and thereby improve the network segmentation performance.
The experimental analysis on the standard DRIVE and CHASE_DB1 databases shows that the sensitivity, specificity, accuracy, and AUC of the parallel fusion and serial embedding methods reach 0.7854, 0.9813, 0.9563, 0.9794; 0.7876, 0.9811, 0.9565, 0.9793 and 0.8110, 0.9737, 0.9547, 0.9667; 0.8113, 0.9717, 0.9574, 0.9750, respectively.
The experimental results show that multiscale feature detection and feature dense connection can effectively enhance the network model's ability to detect blood vessels and improve the network segmentation performance, which is superior to U-Net algorithm and some mainstream retinal blood vessel segmentation algorithms at present.
视网膜血管的分割结果对各种眼科疾病的自动诊断有重要影响。为了进一步提高视网膜血管的分割精度，我们提出了一种基于多尺度血管检测的改进算法，该算法通过密集连接网络提取特征并重用特征。
设计了并行融合和串行嵌入多尺度特征密集连接 U-Net 结构。在并行融合方法中，分别对输入图像的特征进行 Inception 多尺度卷积和密集块卷积提取，然后融合特征并输入到后续网络中。在串行嵌入模式下，将 Inception 多尺度卷积结构嵌入到密集连接网络模块中，然后使用密集连接结构替换 U-Net 网络编码器部分中的经典卷积块，从而实现多尺度特征提取和复杂结构血管的高效利用，从而提高网络分割性能。
在标准 DRIVE 和 CHASE_DB1 数据库上的实验分析表明，并行融合和串行嵌入方法的灵敏度、特异性、准确性和 AUC 分别达到 0.7854、0.9813、0.9563、0.9794；0.7876、0.9811、0.9565、0.9793 和 0.8110、0.9737、0.9547、0.9667；0.8113、0.9717、0.9574、0.9750。
实验结果表明，多尺度特征检测和特征密集连接可以有效增强网络模型检测血管的能力，提高网络分割性能，优于 U-Net 算法和目前一些主流的视网膜血管分割算法。
