A dual-encoder U-Net based algorithm for right ventricle MRI segmentation
10.3969/j.issn.1005-202X.2025.08.007
- VernacularTitle:基于双编码器U-Net的右心室MRI分割算法
- Author:
Weibin DING
1
;
Shaohua JIANG
;
Ting XU
;
Lijuan HUANG
Author Information
1. 湖南师范大学信息科学与工程学院,湖南 长沙 410081
- Publication Type:Journal Article
- Keywords:
right ventricle segmentation;
cardiac magnetic resonance imaging;
feature repurposing;
multi-scale feature;
hybrid loss function
- From:
Chinese Journal of Medical Physics
2025;42(8):1026-1035
- CountryChina
- Language:Chinese
-
Abstract:
The accurate segmentation of the right ventricle is crucial for cardiac disease research,but its low contrast with surrounding tissues and complex structure make segmentation challenging.To address these issues,a dual-encoder segmentation model combining nested multi-scale feature fusion and feature repurposing modules is proposed.Specifically,the nested multi-scale feature fusion module captures boundary detail features through multi-scale dilated convolutions and reduces the semantic gap between the encoder and decoder using short skip connections,while the feature repurposing module enhances feature extraction ability by leveraging fine-grained features from shallow layers.Ablation experiments show that the inclusion of these two modules improves the Dice similarity coefficient of U-Net by 3.14%.On the ACDC dataset,the proposed model achieves a Dice similarity coefficient of 90.31%and a mean Hausdorff distance of 5.21 mm,outperforming other comparative models.Additionally,its generalization ability is validated on the M&Ms dataset.Experimental results demonstrate the excellent performance and robustness of the proposed model in right ventricle segmentation.
