Fully-automatic brain tumor segmentation based on effective receptive field and attention fusion mechanism
10.3969/j.issn.1005-202X.2024.05.006
- VernacularTitle:基于有效感受野和注意力融合机制的脑肿瘤全自动分割
- Author:
Xiang ZOU
1
;
Yu WANG
;
Hongbing XIAO
;
Di YANG
Author Information
1. 北京工商大学计算机与人工智能学院,北京 100048
- Keywords:
brain tumor segmentation;
EAU-Net;
effective receptive field expansion block;
attention fusion module;
inverted residual structure
- From:
Chinese Journal of Medical Physics
2024;41(5):563-570
- CountryChina
- Language:Chinese
-
Abstract:
Despite significant achievements of deep learning in medical image segmentation,there are challenges for brain tumor segmentation using deep learning,such as insufficient receptive field,excessive redundant information,and information loss.To address these issues,a novel brain tumor segmentation network model(EAU-Net)is proposed based on encoder-decoder structure.EAU-Net incorporates an effective receptive field expansion block and an attention fusion module to minimize the adverse effects caused by insufficient receptive field and excessive redundant information which often occurred in the current brain tumor segmentation network.Additionally,a bottleneck resampling module based on inverted residual structure is introduced to effectively avoid information loss during upsampling and downsampling,while deep convolutions are used to reduce computational complexity.Experimental results on the BraTS2020 dataset reveal that EAU-Net achieves the highest segmentation accuracy,demonstrating its feasibility and effectiveness for brain tumor segmentation.
