Cerebrovascular Segmentation Model Based on Spatial Attention-Guided 3D Inception U-Net with Multi-Directional MIPs

Abstract

The segmentation algorithm of cerebrovascular magnetic resonance angiography (MRA) images based on deep learning plays an essential role in medical study. Traditional segmentation algorithms face poor segmentation results and poor connectivity when the cerebrovascular vessels are thinner. An improved segmentation algorithm based on deep convolutional networks is proposed in this research. The proposed segmentation network combines the original 3D U-Net with the maximum intensity projection (MIP), which was transformed from the corresponding patch of a 3D MRA image. The MRA dataset provided by Jeonbuk National University Hospital was used to evaluate the experimental results in comparison with traditional 3D cerebrovascular segmentation methods and other state–of–the–art deep learning methods. The experimental results showed that our method achieved the best test performance among the compared methods in terms of the Dice score when Inception blocks and attention modules were placed in the proposed dual-path networks.

Keywords

• cerebrovascular segmentation
• deep convolutional neural network
• 3D U-Net
• multi-directional
• maximum intensity projection
• MRA images