Design of a high-resolution segmentation network for digital subtraction angiography of cerebral vessels
To solve the problem of low accuracy of existing convolutional neural networks for cerebral vascular DSA image segmentation,an improved network based on U-Net(IC-Net)is proposed.By fusing the use of inception and channel attention modules,rich vascular feature information is extracted using multiple sensory domains and feature information is filtered.A new 7×7 convolutional layer is added to reduce the amount of data generated dur-ing training by compressing the feature layer resolution.Compared with the U-Net and common U-Net improved models,the improved model's intersection over union,accuracy,F1-score,and area under the curve increase by 1.82%,0.014%,1.19%,and 0.73%on average,respectively.The results verify that the IC-Net model remark-ably improves the model's capabilities to detect weak vessels and vessel ends in cerebrovascular digital subtraction angiography images and distinguish artifactual noise.The model provides a strong reference for physicians to identi-fy lesions within cerebrovascular vessels.
国家科技期刊平台
NSTL
万方数据
维普
