Research on segmentation method of brain vessel based on deep learning
10.19745/j.1003-8868.2023173
- VernacularTitle:基于深度学习的脑血管分割方法研究
- Author:
Liang WEN
1
;
Hui SUN
;
Zheng ZOU
;
Guo-Biao LIANG
Author Information
1. 北部战区总医院神经外科,沈阳 110840;中国医科大学,沈阳 110122
- Keywords:
deep learning;
brain vessel segmentation;
brain vessel segmentation model;
magnetic resonance angiography image;
generative adversarial networks;
retinal vascular generative adversarial network
- From:
Chinese Medical Equipment Journal
2023;44(9):1-7
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a deep learning-based cerebrovascular segmentation method to solve the problems of magnetic resonance angiography(MRA)image auto segmentation due to some tiny or overlapped vessels.Methods Generative adversarial networks(GAN)consisting of multiple generators and discriminators were used to construct a brain vessel segmen-tation model(BVSM).Firstly,the feature fusion and attention mechanism modules were introduced into the generator network to segment and extract the patient's MRA images;secondly,the discriminator network judged the gap between the brain vessel segmentation results respectively by the generator network and the expert's manual operation,so as to optimize the generator network continuously to obtain realistic segmentation images;finally,the MIDAS dataset was used to design ablation experi-ments to compare the cerebrovascular segmentation results of BVSM with the original model(RVGAN retinal vascular gene-rative adversarial network model),the RVGAN+Attention model incorporated with the attention module and the RVGAN+slice-level feature aggregation(SFA)model with the SFA module in terms of Dice coefficient,accuracy,sensitivity and AUC.Results The BVSM behaved better than the RVGAN,RVGAN+Attention and RVGAN+SFA models with Dice coefficient being 87.2％,accuracy being 88.3％,sensitivity being 86.3％and AUC being 0.942.Conclusion The method proposed facilitates the observation of cerebrovascular structure with high accuracy,and provides an auxiliary means for diagnosing cerebrovascular diseases.[Chinese Medical Equipment Journal,2023,44(9):1-7]
