Segmentation of anterior cruciate ligament images by fusing inflated convolution and residual hybrid attention.
10.7507/1001-5515.202410042
- Author:
Peng SHANG
1
;
Yuling WANG
1
Author Information
1. College of Information Engineering, East China University of Technology, Nanchang 330000, P.R. China.
- Publication Type:Journal Article
- Keywords:
Anterior cruciate ligament;
Dilated convolution;
Hybrid attention mechanism;
Medical image segmentation;
U-Net
- MeSH:
Humans;
Magnetic Resonance Imaging/methods*;
Anterior Cruciate Ligament/diagnostic imaging*;
Image Processing, Computer-Assisted/methods*;
Knee Joint/diagnostic imaging*;
Neural Networks, Computer;
Algorithms;
Deep Learning
- From:
Journal of Biomedical Engineering
2025;42(2):246-254
- CountryChina
- Language:Chinese
-
Abstract:
Aiming at the problems of low accuracy and large difference of segmentation boundary distance in anterior cruciate ligament (ACL) image segmentation of knee joint, this paper proposes an ACL image segmentation model by fusing dilated convolution and residual hybrid attention U-shaped network (DRH-UNet). The proposed model builds upon the U-shaped network (U-Net) by incorporating dilated convolutions to expand the receptive field, enabling a better understanding of the contextual relationships within the image. Additionally, a residual hybrid attention block is designed in the skip connections to enhance the expression of critical features in key regions and reduce the semantic gap, thereby improving the representation capability for the ACL area. This study constructs an enhanced annotated ACL dataset based on the publicly available Magnetic Resonance Imaging Network (MRNet) dataset. The proposed method is validated on this dataset, and the experimental results demonstrate that the DRH-UNet model achieves a Dice similarity coefficient (DSC) of (88.01±1.57)% and a Hausdorff distance (HD) of 5.16±0.85, outperforming other ACL segmentation methods. The proposed approach further enhances the segmentation accuracy of ACL, providing valuable assistance for subsequent clinical diagnosis by physicians.
