ArticleIEEE transactions on medical imaging2014
Adaptive quantification and longitudinal analysis of pulmonary emphysema with a hidden Markov measure field model.
Article in IEEE transactions on medical imaging, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Computed Tomography Densitometry for the Assessment of Emphysema in Clinical Trials: A Position Paper from the Fleischner Society.American journal of respiratory and critical care medicine · 2025Pooled it
- Segmentation-based deep learning emphysema quantification using chest CT: improved accuracy and robustness vs LAA-950.European radiology · 2026Article
- A self-supervised framework for emphysema anomaly detection and staging in computed tomography scans.Patterns (New York, N.Y.) · 2026Article
- Article
- ROBUST QUANTIFICATION OF PERCENT EMPHYSEMA ON CT VIA DOMAIN ATTENTION: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA) LUNG STUDY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- MULTI-VIEW CNN FOR TOTAL LUNG VOLUME INFERENCE ON CARDIAC COMPUTED TOMOGRAPHY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study.IEEE transactions on medical imaging · 2021Article
- Review
- Article
- UNSUPERVISED DOMAIN ADAPTION WITH ADVERSARIAL LEARNING (UDAA) FOR EMPHYSEMA SUBTYPING ON CARDIAC CT SCANS: THE MESA STUDY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2019Article
- Using a spatial point process framework to characterize lung computed tomography scans.Spatial statistics · 2019Article
- Article
- Unsupervised Discovery of Spatially-Informed Lung Texture Patterns for Pulmonary Emphysema: The MESA COPD Study.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2017Article
- Pulmonary Lobe Segmentation With Probabilistic Segmentation of the Fissures and a Groupwise Fissure Prior.IEEE transactions on medical imaging · 2017Article
- GENERATIVE METHOD TO DISCOVER EMPHYSEMA SUBTYPES WITH UNSUPERVISED LEARNING USING LUNG MACROSCOPIC PATTERNS (LMPS): THE MESA COPD STUDY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2017Article
- Computational identification of putative lincRNAs in mouse embryonic stem cell.Scientific reports · 2016Article
- Emphysema Quantification on Cardiac CT Scans Using Hidden Markov Measure Field Model: The MESA Lung Study.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2016Article
- A Robust Emphysema Severity Measure Based on Disease Subtypes.Academic radiology · 2016Article
- DERIVATION OF A TEST STATISTIC FOR EMPHYSEMA QUANTIFICATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2016Article
- Adaptive quantification and longitudinal analysis of pulmonary emphysema with a hidden Markov measure field model.IEEE transactions on medical imaging · 2014Article
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Authors and funding
5 authors.
Funding
Abstract
The extent of pulmonary emphysema is commonly estimated from CT scans by computing the proportional area of voxels below a predefined attenuation threshold. However, the reliability of this approach is limited by several factors that affect the CT intensity distributions in the lung. This work presents a novel method for emphysema quantification, based on parametric modeling of intensity distributions and a hidden Markov measure field model to segment emphysematous regions. The framework adapts to the characteristics of an image to ensure a robust quantification of emphysema under varying CT imaging protocols, and differences in parenchymal intensity distributions due to factors such as inspiration level. Compared to standard approaches, the presented model involves a larger number of parameters, most of which can be estimated from data, to handle the variability encountered in lung CT scans. The method was applied on a longitudinal data set with 87 subjects and a total of 365 scans acquired with varying imaging protocols. The resulting emphysema estimates had very high intra-subject correlation values. By reducing sensitivity to changes in imaging protocol, the method provides a more robust estimate than standard approaches. The generated emphysema delineations promise advantages for regional analysis of emphysema extent and progression.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.