Infarction lesion segmentation method for stroke DWI images based on multi-head self-attention mechanism and U-Net
10.19745/j.1003-8868.2024228
- VernacularTitle:基于多头自注意力机制与U-Net的脑卒中DWI图像梗死病灶分割方法研究
- Author:
Xiao-yan LU
1
;
Hua-wei ZHANG
;
Jing-li GUO
;
Qun GUO
;
Hong-bing JIANG
;
Yuan ZHANG
Author Information
1. 南京医科大学附属南京医院(南京市第一医院)医学影像科,南京 210006
- Publication Type:Journal Article
- Keywords:
multi-head self-attention mechanism;
U-Net;
stroke;
ischemic stroke;
infarction lesion;
diffusion weighted imaging image;
lesion segmentation
- From:
Chinese Medical Equipment Journal
2024;45(12):9-13
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a segmentation method based on multi-head self-attention(MHSA)mechanism and U-net for solving the problems of stroke diffusion weighted imaging(DWI)images in infarction lesion segmentation due to blurred boundary and small area.Methods U-Net was used as the basic segmentation model,and the MHSA module was added after the last convolution operation of its encoder to build a MHSA-UNet segmentation model.The MHSA-UNet segmentation model had its effectiveness verified by being trained and validated on a self-constructed dataset and compared with the U-Net model and the Attention U-Net model for the segmentation of infarction lesions in DWI images of stroke.Results The MHSA-UNet segmentation model behaved generally better than U-Net model and Attention U-Net model,whose Dice similarity coefficient,intersection over union and 95％Hausdorff distance of the MHSA-UNet model were 0.790,0.571 and 9.982,respectively.Conclusion The proposed method segments infarction lesions in stroke DWI images effectively,and can assist clinicians in disease diagnosis.[Chinese Medical Equipment Journal,2024,45(12):9-13]
