超越像素:通过传统的机器学习和图形卷积网络进行基于超像素的MRI细分
Zakia Khatun1, Halldór Jónsson2, Mariella Tsirilaki3
1Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Salerno, Italy; Institute of Biomedical and Neural Engineering, Department of Engineering, Reykjavik University, Reykjavik, Iceland.
Computer methods and programs in biomedicine
|September 5, 2024
概括
这项研究引入了一种基于超像素的新方法,用于MRI扫描中的阿基里斯肌细分. 随机森林和SVM方法实现了高精度,在有限的数据上超过了图形卷积网络方法.
科学领域:
- 医学成像分析 医学成像分析
- 计算解剖学的计算解剖学
- 生物医学工程 生物医学工程
背景情况:
- 精确的肌细分对于诊断诸如肌病变等病理至关重要.
- 自动化方法提高了特定肌区域的详细分析.
- 阿基里斯肌是人体最大的肌,是细分研究的一个重点.
研究的目的:
- 使用MRI数据开发和评估一个端到端模块,用于阿基里斯肌细分.
- 为了比较超像素分类 (随机森林,SVM) 与基于图形的卷积网络 (GCN) 的效率,用于肌细分.
主要方法:
- 一种两阶段的方法,涉及初步基于超像素的粗细分,随后进行最终分类.
- 超级像素使用随机森林 (RF) 和支持矢量机 (SVM) 算法进行分类.
- 一种替代方法将超像素排列转换为图形,用于使用基于图形的卷积网络 (GCN) 进行分类.
主要成果:
- 在未见的测试数据上,RF和SVM方法在ROC曲线下的面积 (AUC) 分别达到0.992和0.987的高得分.
- 对射频和SVM的灵敏度分别为0.904和0.966,在识别肌像素方面表现强.
- GCN方法的AUC值为0.933,灵敏度为0.899,表明该数据集的表现良好,但相对较低.
结论:
- 超像素生成是一种有效的粗细分技术,用于肌细分.
- 不基于图形的超像素分类方法 (RF,SVM) 在有限的数据集上显示出高于GCN方法的性能.
- 这项研究为肌细分提供了宝贵的见解,并突出了未来研究和模型改进的途径.
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