基于空间信息肺纹理学习的肺气肿新亚型
Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning
摘要：
肺气肿与慢性阻塞性肺疾病（COPD）有相当大的重叠，传统上分为三个亚型，以前在尸检中确定。在计算机断层扫描（CT）上对肺气肿亚型的无监督学习为肺气肿亚型的新定义开辟了道路，并消除了彻底手动标记的需要。然而，基于CT的肺气肿亚型仅限于基于纹理的模式，而不考虑空间位置。在这项工作中，我们引入了一个标准化的空间映射的肺定量研究的肺纹理位置，并提出了一个新的框架相结合的空间和纹理信息，发现空间通知肺纹理模式（SLTP），代表新的肺气肿亚型。利用来自MESA COPD和EMCAP研究的两个队列的全肺CT扫描，我们首先表明我们的空间映射使肺气肿空间位置的人群范围的研究成为可能。然后，我们评估了在MESA COPD中发现的sLTP的特征，并表明它们是可重复的，能够编码标准的肺气肿亚型，并与生理症状相关。
英文摘要：
Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location, and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtypes. Exploiting two cohorts of full-lung CT scans from the MESA COPD and EMCAP studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.
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