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在多发性硬化症中使用无监督机器学习进行自动活跃损伤跟踪.

Jason Uwaeze¹, Ponnada A Narayana², Arash Kamali²

¹Department of Computer Science, Rice University, Houston, TX 77005, USA.

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|March 27, 2024

概括

此摘要是机器生成的。

非线性维度减小 (NLDR) 方法,Isomap和局部线性嵌入 (LLE),可以有效地识别多发性硬化症 (MS) 脑MRI中的活跃病变,而无需使用对比剂. 这些不那么数据密集的技术显示出临床决策支持的前景.

关键词:

减少维度,减少维度.损伤细分损伤细分多参数核磁共振成像 (MRI)多发性硬化症多发性硬化症

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科学领域:

医学成像分析医学成像分析
机器学习在放射学中的应用
神经科学研究神经科学研究

背景情况:

在多发性硬化症 (MS) 磁共振成像 (MRI) 中精确识别活跃病变对于患者的诊断和治疗至关重要.
目前的方法依赖于基于加多的对比剂 (GBCA),这引发了对组织积累,患者安全和医疗保健成本的担忧.
现有的非对比性MRI技术用于积极的病变检测通常是数据密集型的深度学习方法.

研究的目的:

实施和评估非线性维度减小 (NLDR) 方法,特别是局部线性嵌入 (LLE) 和Isomap.
评估LLE和Isomap在自动识别大脑MRI上活跃的MS病变的有效性,而无需使用对比剂.
探索不那么数据密集的替代方案,以现有的深度学习方法,以积极的病变检测.

主要方法:

使用了多参数MRI数据集,包括FLAIR,T2加权,质子密度加权和前/后对比T1加权图像.
使用无监督的LLE和Isomap算法,将多参数MRI数据的维度减少到一个嵌入式图像中.
通过使用子相似度指数 (DS) 量化NLDR方法的性能,将确定的病变与专家标记的基底真相进行比较.

主要成果:

将LLE和Isomap应用于40名多发性硬化症患者的中位数DS分数分别为0.74±0.1和0.78±0.09.
与当前最先进的非对比技术相比,这两种NLDR方法都表现出更高的性能.
该研究成功地在非对比增强的MRI扫描上识别了活跃的MS病变.

结论:

非线性维度减小方法,Isomap和LLE,对于在非对比MRI上识别活跃的MS病变是有效和可行的.
这些NLDR技术为MS的活跃病变检测提供了一个有希望的,不那么数据密集的方法.
伊索马普和LLE具有作为神经科医生和放射科医生的宝贵临床决策支持工具的潜力.