Pulmonary emphysema, the progressive, irreversible loss of lung tissue, is
conventionally categorized into three subtypes identifiable on pathology and on
lung computed tomography (CT) images. Recent work has led to the unsupervised
learning of ten spatially-informed lung texture patterns (sLTPs) on lung CT,
representing distinct patterns of emphysematous lung parenchyma based on both
textural appearance and spatial location within the lung, and which aggregate
into 6 robust and reproducible CT Emphysema Subtypes (CTES). Existing methods
for sLTP segmentation, however, are slow and highly sensitive to changes in CT
acquisition protocol. In this work, we present a robust 3-D
squeeze-and-excitation CNN for supervised classification of sLTPs and CTES on
lung CT. Our results demonstrate that this model achieves accurate and
reproducible sLTP segmentation on lung CTscans, across two independent cohorts
and independently of scanner manufacturer and model.

通过 3-D 挤压和激励卷积神经网络，在肺部计算机断层扫描中实现了对空间统计学信息感知的肺纹理模式和肺气肿亚型的准确分类。

使用压榨与激发卷积神经网络进行稳健的放射性肺气肿亚型标签化：MESA Lung 和 SPIROMICS 研究

Robust deep labeling of radiological emphysema subtypes using squeeze  and excitation convolutional neural networks: The MESA Lung and SPIROMICS  Studies

Robust quantification of pulmonary emphysema on computed tomography (CT)
remains challenging for large-scale research studies that involve scans from
different scanner types and for translation to clinical scans. Existing studies
have explored several directions to tackle this challenge, including density
correction, noise filtering, regression, hidden Markov measure field (HMMF)
model-based segmentation, and volume-adjusted lung density. Despite some
promising results, previous studies either required a tedious workflow or
limited opportunities for downstream emphysema subtyping, limiting efficient
adaptation on a large-scale study. To alleviate this dilemma, we developed an
end-to-end deep learning framework based on an existing HMMF segmentation
framework. We first demonstrate that a regular UNet cannot replicate the
existing HMMF results because of the lack of scanner priors. We then design a
novel domain attention block to fuse image feature with quantitative scanner
priors which significantly improves the results.

通过融合图像特征和定量扫描仪先验信息，我们开发了一个基于深度学习的端到端框架来准确量化计算机断层扫描（CT）中的肺气肿，并在大规模研究和临床应用中取得了显著改进。