Densely Connected U Net Retinal Vessel Segmentation Algorithm Based on Multi scale Feature Convolution Extraction.
PURPOSE 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 multi scale vessel detection, which extracts features through densely connected networks and reuses features. METHODS A parallel fusion and serial embedding multi scale feature dense connection U Net structure are designed. In the parallel fusion method, features of the input images are extracted for Inception multi scale convolution and dense block convolution respectively, and then the features are fused and input into the subsequent network. In serial embedding mode, the Inception multi scale 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 multi scale feature extraction and efficient utilization of complex structure vessels and thereby improve the network segmentation performance. RESULTS 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. CONCLUSIONS The experimental results show that multi scale 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.
Medical physics