LRU-Net：用于磁共振成像（MRI）图像中前交叉韧带（ACL）撕裂区域定位的轻量级多尺度特征提取方法
LRU-Net: lightweight and multiscale feature extraction for localization of ACL tears region in MRI images.
作者信息
Si Xiaojun, Yan Liang, Shi Cui, Xu Yang
机构信息
Department of Information Center, Affiliated Hospital of Nantong University, Nantong, China.
Department of Orthopedics, Nantong Rici Hospital Affiliated to Yangzhou University, Nantong, Jiangsu, China.
出版信息
Front Physiol. 2025 Jul 15;16:1611267. doi: 10.3389/fphys.2025.1611267. eCollection 2025.
INTRODUCTION
Anterior cruciate ligament (ACL) injuries hold significant clinical importance, making the development of accurate and efficient diagnostic tools essential. Deep learning has emerged as an effective method for detecting ACL tears. However, current models often struggle with multiscale and boundary-sensitive tear patterns and tend to be computationally intensive.
METHODS
We present LRU-Net, a lightweight residual U-Net designed for ACL tear segmentation. LRU-Net integrates an advanced attention mechanism that emphasizes gradients and leverages the anatomical position of the ACL, thereby improving boundary sensitivity. Furthermore, it employs a dynamic feature extraction module for adaptive multiscale feature extraction. A dense decoder featuring dense connections enhances feature reuse.
RESULTS
In experimental evaluations, LRU-Net achieves a Dice Coefficient Score of 97.93% and an Intersection over Union (IoU) of 96.40%.
DISCUSSION
It surpasses benchmark models such as Attention-Unet, Attention-ResUnet, InceptionV3-Unet, Swin-UNet, Trans-UNet and Rethinking ResNets. With a reduced computational footprint, LRU-Net provides a practical and highly accurate solution for the clinical analysis of ACL tears.
引言
前交叉韧带（ACL）损伤具有重大临床意义，因此开发准确有效的诊断工具至关重要。深度学习已成为检测ACL撕裂的有效方法。然而，当前模型在处理多尺度和边界敏感的撕裂模式时常常遇到困难，并且计算量往往很大。
方法
我们提出了LRU-Net，这是一种专为ACL撕裂分割设计的轻量级残差U-Net。LRU-Net集成了一种先进的注意力机制，该机制强调梯度并利用ACL的解剖位置，从而提高边界敏感性。此外，它采用了一个动态特征提取模块进行自适应多尺度特征提取。具有密集连接的密集解码器增强了特征重用。
结果
在实验评估中，LRU-Net的骰子系数得分为97.93%，交并比（IoU）为96.40%。
讨论
它超越了诸如Attention-Unet、Attention-ResUnet、InceptionV3-Unet、Swin-UNet、Trans-UNet和Rethinking ResNets等基准模型。LRU-Net计算量减少，为ACL撕裂的临床分析提供了一种实用且高度准确的解决方案。
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