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

Yongwei Liu,Hyo-Sung Kwak,Il-Seok Oh

doi:10.3390/app12052288

Yongwei Liu, Hyo-Sung Kwak + Show 1 more

Open Access

https://doi.org/10.3390/app12052288

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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.

Highlights

The incidence and risk of cerebrovascular diseases are very high, posing a growing threat to human life and health
Inspired by the idea of Inception V2, we propose a new architecture based on 3D U-Net and decomposed convolutional Inception block [28], in which each convolutional layer in the original U-Net is substituted with an Inception block
Since the magnetic resonance angiography (MRA) values were non-standardized, we applied Z-score normalization to each MRA image from each patient; Z-score normalization uses the cerebrovascular mask for the image I(x) to determine the mean μz-score and standard deviation σz-score of the intensities inside the brain mask

Summary

Introduction

The incidence and risk of cerebrovascular diseases are very high, posing a growing threat to human life and health. Diagnosis and treatment can effectively curb the development of cerebrovascular diseases. The precise segmentation of cerebrovascular diseases is the basis of the auxiliary diagnosis of cerebrovascular diseases. Cerebrovascular segmentation is of great significance to the auxiliary diagnosis of cerebrovascular diseases and human health. Cerebrovascular segmentation could reduce the workload of doctors in reading diagnostic images significantly and provide doctors with more accurate disease information. Compared with traditional organ segmentation, segmenting the cerebrovascular structure from MRA images is extremely challenging due to various difficulties, such as noise, thin or blurred vascular shapes, background noise, and low contrast between the cerebrovascular vessels and the background

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