LRAE-Unet:a lightweight network for fully automatic segmentation of brain tumor from MRI
10.3969/j.issn.1005-202X.2024.01.006
- VernacularTitle:LRAE-Unet:轻量级MRI脑肿瘤全自动分割网络
- Author:
Jiahao LIN
1
;
Yu WANG
;
Hongbing XIAO
;
Mei SUN
Author Information
1. 北京工商大学人工智能学院,北京 100048
- Keywords:
brain tumor;
LRAE-Unet;
lightweight residual module;
lightweight self-attention module;
average pooling module
- From:
Chinese Journal of Medical Physics
2024;41(1):43-49
- CountryChina
- Language:Chinese
-
Abstract:
A lightweight residual attention enhanced Unet(LRAE-Unet)is designed for the fully automatic brain tumor segmentation.LRAE-Unet uses lightweight residual module to solve the problems of gradient disappearance and network degradation when the network layers increases,lightweight self-attention module to suppress the irrelevant areas and highlight the significant features of specific local areas,and enhanced average pooling module with a larger field of perception to reduce the space of feature map,save computing resources and avoid over-fitting.The experiment on BraTS 2019 dataset shows that the proposed method has a Dice similarity coefficient of 91.24%,88.64%and 88.32%in the segmentations of the whole tumor,tumor core and enhanced tumor,which proves its feasibility and effectiveness for brain tumor segmentation.
