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密集的多尺度图形卷积网络用于膝关节关节软骨细分
Christos Chadoulos1, Dimitrios Tsaopoulos2, Andreas Symeonidis1
1Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Bioengineering (Basel, Switzerland)
|March 27, 2024
概括
我们开发了一种密集的多尺度自适应图形卷积网络 (DMA-GCN),用于从MRI图像中准确地细分膝盖软骨. 这种新的方法整合了本地和全球的学习,在细分精度上表现优于现有的技术.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 从MRI图像中精确细分膝关节软骨对于诊断骨关节炎至关重要.
- 现有的方法经常与复杂的解剖结构和图像质量的变化作斗争.
研究的目的:
- 提出一种新的密集多尺度自适应图形卷积网络 (DMA-GCN),用于膝关节关节软骨的自动细分.
- 用骨关节炎倡议 (OAI) 队列来评估DMA-GCN与传统和基于深度学习的方法的性能.
主要方法:
- DMA-GCN通过交替或连续的卷积单位组合来整合本地和全球学习.
- 有剩余跳过连接的密集连接架构可以实现更深的网络结构,以增强功能表示.
- 适应式图形学习机制允许在训练期间自动学习图形结构.
主要成果:
- 与竞争方法相比,DMA-GCN在所有评估指标上取得了卓越的表现.
- 该方法证明了高分段精度,子相似系数 (DSC) 为骨95.71%,骨94.02%.
- 彻底的实验分析调查了各种因素对分类率的影响.
结论:
- 拟议的DMA-GCN方法为自动从MRI图像中对膝关节软骨进行细分提供了显著的进步.
- DMA-GCN能够整合多个规模的本地和全球信息的能力,加上自适应式图形学习,带来了最先进的性能.
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