Naik Sneha N, Angelini Elsa D, Barr R Graham, Allen Norrina, Bertoni Alain, Hoffman Eric A, Manichaikul Ani, Pankow Jim, Post Wendy, Sun Yifei, Watson Karol, Smith Benjamin M, Laine Andrew F
Columbia University, USA.
Telecom Paris LTCI, France.
Proc IEEE Int Symp Biomed Imaging. 2024 May;2024. doi: 10.1109/isbi56570.2024.10635651. Epub 2024 Aug 22.
High-resolution full lung CT scans now enable the detailed segmentation of airway trees up to the 6th branching generation. The airway binary masks display very complex tree structures that may encode biological information relevant to disease risk and yet remain challenging to exploit via traditional methods such as meshing or skeletonization. Recent clinical studies suggest that some variations in shape patterns and caliber of the human airway tree are highly associated with adverse health outcomes, including all-cause mortality and incident COPD. However, quantitative characterization of variations observed on CT segmented airway tree remain incomplete, as does our understanding of the clinical and developmental implications of such. In this work, we present an unsupervised deep-learning pipeline for feature extraction and clustering of human airway trees, learned directly from projections of 3D airway segmentations. We identify four reproducible and clinically distinct airway sub-types in the MESA Lung CT cohort.
高分辨率全肺CT扫描现在能够对直至第六代分支的气道树进行详细分割。气道二值掩码显示出非常复杂的树形结构，这些结构可能编码与疾病风险相关的生物学信息，但通过诸如网格化或骨架化等传统方法来利用这些信息仍然具有挑战性。最近的临床研究表明，人类气道树的形状模式和管径的一些变化与不良健康结果高度相关，包括全因死亡率和慢性阻塞性肺疾病（COPD）的发病。然而，对CT分割气道树上观察到的变化的定量表征仍然不完整，我们对其临床和发育意义的理解也是如此。在这项工作中，我们提出了一种无监督深度学习管道，用于从3D气道分割的投影中直接学习人类气道树的特征提取和聚类。我们在多族裔动脉粥样硬化研究（MESA）肺部CT队列中识别出四种可重复且在临床上有明显差异的气道亚型。
