Parallel dual-branch fusion network for retinal vessel segmentation
It existed the weakness of low segmentation accuracy for capillaries in retinal images with complex topology and low contrast with current deep learning models and the segmentation results were discontinuous.To address these problems,a parallel dual-branch fusion network based on deep learning was proposed,which was consisted of two parts:parallel dual-branch module and fusion module.The parallel dual-branch module was designed to use two U-shaped branches:pixel-level segmentation branch and centerline-level segmentation branch,aiming to obtain thick and thin vessels in retinal images respectively.The fusion module was built based on spatial attention which was used to further optimize the preliminary segmentation map output by the parallel dual-branch module and obtain more detail features of capillaries.The experimental results on SVC dataset of ROSE-1 showed that the proposed model performed excellently in all evaluation indicators,among which the area under the ROC curve(AUC)reached 93.63％,and the accuracy(ACC)reached 92.25％.
万方数据
维普
