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在高分辨率CT: THE MESA LUNG STUDY上无人监督的气道树结构的集群:

Artur Wysoczanski¹, Elsa D Angelini^1,2, Benjamin M Smith^3,4

¹Department of Biomedical Engineering, Columbia University, New York, NY, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|October 14, 2024

概括

人的呼吸道分支模式有很大差异,影响了肺部疾病的风险. 本研究使用无监督聚类来识别不同的气道树亚型,有助于理解它们的临床影响.

科学领域:

肺部医学肺部医学
计算生物学计算生物学
医学成像分析医学成像分析

背景情况:

人类呼吸道树形态表现出显著的个体间变异性.
已知的呼吸道分支模式变异与慢性阻塞性肺部疾病 (COPD) 的风险增加以及肺部发育中的遗传因素有关.
气道树几何形状和拓学的变化的临床意义尚未完全理解.

研究的目的:

开发一种无监督的集群方法来分析人类气道树结构.
在人类群体中识别出不同的和可复制的气道树亚型.
为了解气道形态变化的临床影响奠定基础.

主要方法:

使用Billera-Holmes-Vogtmann (BHV) 树空间来表示气道树结构.
开发并验证了使用合成气道树数据的无监督集群管道.
将验证的算法应用于来自动脉样硬化多民族研究 (MESA) 肺部CT队列的气道树数据.

主要成果:

在合成数据上成功验证了无监督集群管道.
在MESA肺部CT队列中确定了形态上不同的气道树亚型.
证明了已识别的气道树亚型的可复制性.

结论:

开发的无监督集群方法有效地识别出不同的气道树亚型.
这种方法为表征气道形态变异提供了一种新的方法.
进一步的研究可以探索这些已识别的呼吸道亚型与COPD等肺部疾病相关的临床相关性.

关键词:

气道形态学气道形态学社区检测发现计算解剖学的计算解剖学肺部CT 肺部CT 肺部CT

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