  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.
