Robust Quantification of Percent Emphysema on CT via Domain Attention: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study

Xuzhe Zhang; Elsa D Angelini; Eric A Hoffman; Karol E Watson; Benjamin M Smith; R. Graham Barr; Andrew F Laine

doi:10.48550/arxiv.2402.18383

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Robust Quantification of Percent Emphysema on CT via Domain Attention: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study

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Robust Quantification of Percent Emphysema on CT via Domain Attention: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study

Xuzhe Zhang, Elsa D Angelini, Eric A Hoffman, Karol E Watson, Benjamin M Smith, R. Graham Barr and Andrew F Laine

arXiv.org

Cornell University

02/28/2024

DOI: 10.48550/arxiv.2402.18383

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

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.

Computer Science - Computer Vision and Pattern Recognition

Details

Title: Subtitle: Robust Quantification of Percent Emphysema on CT via Domain Attention: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study
Creators: Xuzhe Zhang - Columbia University
Elsa D Angelini - Columbia University
Eric A Hoffman - University of Iowa
Karol E Watson - David Geffen School of Medicine at UCLA
Benjamin M Smith - Columbia University Irving Medical Center
R. Graham Barr - Columbia University Irving Medical Center
Andrew F Laine - Columbia University
Resource Type: Preprint
Publication Details: arXiv.org
DOI: 10.48550/arxiv.2402.18383
eISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 02/28/2024
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9984562589202771

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