Robust Quantification of Percent Emphysema on CT via Domain Attention: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911789953843200 |
|---|---|
| author | Zhang, Xuzhe Angelini, Elsa D. Hoffman, Eric A. Watson, Karol E. Smith, Benjamin M. Barr, R. Graham Laine, Andrew F. |
| author_facet | Zhang, Xuzhe Angelini, Elsa D. Hoffman, Eric A. Watson, Karol E. Smith, Benjamin M. Barr, R. Graham Laine, Andrew F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18383 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Quantification of Percent Emphysema on CT via Domain Attention: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study Zhang, Xuzhe Angelini, Elsa D. Hoffman, Eric A. Watson, Karol E. Smith, Benjamin M. Barr, R. Graham Laine, Andrew F. Computer Vision and Pattern Recognition 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. |
| title | Robust Quantification of Percent Emphysema on CT via Domain Attention: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.18383 |