.png){kind=icon}
返回
A learnable Gabor Convolution kernel for vessel segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compbiomed.2023.106892.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Vessel segmentation is significant for characterizing vascular diseases, receiving wide attention of researchers. The common vessel segmentation methods are mainly based on convolutional neural networks (CNNs), which have excellent feature learning capabilities. Owing to inability to predict learning direction, CNNs generate large channels or sufficient depth to obtain sufficient features. It may engender redundant parameters. Drawing on performance ability of Gabor filters in vessel enhancement, we built Gabor convolution kernel and designed its optimization. Unlike traditional filter using and common modulation, its parameters are automatically updated using gradients in the back propagation. Since the structural shape of Gabor convolution kernels is the same as that of regular convolution kernels, it can be integrated into any CNNs architecture. We built Gabor ConvNet using Gabor convolution kernels and tested it using three vessel datasets. It scored 85.06%, 70.52% and 67.11%, respectively, ranking first on three datasets. Results shows that our method outperforms advanced models in vessel segmentation. Ablations also proved that Gabor kernel has better vessel extraction ability than the regular convolution kernel.
Keyword:
Vessel segmentation
Gabor
Convolutional neural network
Convolution kernel
Deep learning
期刊
IF:
6.3
论文数:
8.3K
被引数:
3.3W
机构
暂无机构信息
引用论文
DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images.png)
DIAGNOSTICS
OAAI
DIAGNOSTICSIF3.3
OAAI分享收藏An effective approach for the protection of user commodity viewing privacy in e-commerce website
PREAI
分享收藏
查看更多内容