Uncertainty-aware quantitative CT evaluation of emphysema and mortality risk from variable radiation dose images

Heron MP (2021) Deaths: leading causes for 2018. Natl Vital Stat Rep 70:1–115

Lynch DA, Austin JHM, Hogg JC et al (2015) CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner Society. Radiology 277:192–205

Baraghoshi D, Strand M, Humphries SM et al (2023) Quantitative CT evaluation of emphysema progression over 10 years in the COPDGene study. Radiology 307:e222786

Article  PubMed  Google Scholar 

Coxson HO (2013) Sources of variation in quantitative computed tomography of the lung. J Thorac Imaging 28:272–279

Article  PubMed  Google Scholar 

Ash SY, San José Estépar R, Fain SB et al (2021) Relationship between emphysema progression at CT and mortality in ever-smokers: results from the COPDGene and ECLIPSE cohorts. Radiology 299:222–231

Article  PubMed  Google Scholar 

Hatt C, Galban C, Labaki W, Kazerooni E, Lynch D, Han M (2018) Convolutional neural network based COPD and emphysema classifications are predictive of lung cancer diagnosis. In: Image analysis for moving organ, breast, and thoracic images. Springer, Cham, pp 302–309

Humphries SM, Notary AM, Centeno JP et al (2019) Deep learning enables automatic classification of emphysema pattern at CT. Radiology 294:434–444

Article  PubMed  Google Scholar 

Xie W, Jacobs C, Charbonnier JP, Slebos DJ, van Ginneken B (2023) Emphysema subtyping on thoracic computed tomography scans using deep neural networks. Sci Rep 13:14147

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kendall A, Gal Y, Cipolla R (2018) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7482–7491

Ruder S (2017) An overview of multi-task learning in deep neural networks. Preprint at https://arxiv.org/abs/1706.05098

Ghoshal B, Tucker A, Sanghera B, Lup Wong W (2021) Estimating uncertainty in deep learning for reporting confidence to clinicians in medical image segmentation and diseases detection. Comput Intell 37:701–734

Article  Google Scholar 

Kendall A, Gal Y (2017) What uncertainties do we need in Bayesian deep learning for computer vision? In: Proceedings of NIPS'17, pp 5580–5590

Shridhar KLF, Liwicki M (2019) A comprehensive guide to Bayesian convolutional neural network with variational inference. Preprint at https://arxiv.org/abs/1901.02731

Chen X, Liu C, Zhao Y, Jia Z, Jin G (2022) Improving adversarial robustness of Bayesian neural networks via multi-task adversarial training. Inf Sci 592:156–173

Article  Google Scholar 

Cifci MA (2023) A deep learning-based framework for uncertainty quantification in medical imaging using the DropWeak technique: an empirical study with Baresnet. Diagnostics 13:800

Regan EA, Hokanson JE, Murphy JR et al (2010) Genetic epidemiology of COPD (COPDGene) study design. COPD 7:32–43

Article  PubMed  Google Scholar 

QIBA Lung Density Biomarker Committee (2019) QIBA profile: computed tomography: lung densitometry. Available via https://qibawiki.rsna.org/images/e/e4/QIBA_CT_Lung_Density_Profile_090319-clean.pdf. Accessed 19 July 2022

Hatt CR, Oh AS, Obuchowski NA, Charbonnier JP, Lynch DA, Humphries SM (2021) Comparison of CT lung density measurements between standard full-dose and reduced-dose protocols. Radiol Cardiothorac Imaging 3:e200503

Article  PubMed  PubMed Central  Google Scholar 

Shaker SB, Dirksen A, Laursen LC, Skovgaard LT, Holstein-Rathlou NH (2004) Volume adjustment of lung density by computed tomography scans in patients with emphysema. Acta Radiol 45:417–423

Article  CAS  PubMed  Google Scholar 

Stoel BC, Putter H, Bakker ME et al (2008) Volume correction in computed tomography densitometry for follow-up studies on pulmonary emphysema. Proc Am Thorac Soc 5:919–924

Article  PubMed  Google Scholar 

Hoffman EA, Ahmed FS, Baumhauer H et al (2014) Variation in the percent of emphysema-like lung in a healthy, nonsmoking multiethnic sample. The MESA lung study. Ann Am Thorac Soc 11:898–907

Article  PubMed  PubMed Central  Google Scholar 

Blei DM, Kucukelbir A, McAuliffe JD (2017) Variational inference: a review for statisticians. J Am Stat Assoc 112:859–877

Article  CAS  Google Scholar 

Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980

Kwon Y, Won J-H, Kim BJ, Paik MC (2020) Uncertainty quantification using Bayesian neural networks in classification: application to biomedical image segmentation. Comput Stat Data Anal 142:106816

Article  Google Scholar 

Kendall A, Badrinarayanan V, Cipolla R (2015) Bayesian SegNet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. Preprint at https://arxiv.org/abs/1511.02680

Tian X, Samei E (2016) Accurate assessment and prediction of noise in clinical CT images. Med Phys 43:475–482

Article  PubMed  Google Scholar 

Marine S, Stephen LB, Irène B (2007) Partial-volume effect in PET tumor imaging. J Nucl Med 48:932

Article  Google Scholar 

Sánchez-Ferrero GV, Díaz AA, Ash SY et al (2024) Quantification of emphysema progression at CT using simultaneous volume, noise, and bias lung density correction. Radiology 310:e231632

Article  Google Scholar 

Manichaikul A, Hoffman EA, Smolonska J et al (2014) Genome-wide study of percent emphysema on computed tomography in the general population. The Multi-Ethnic Study of Atherosclerosis Lung/SNP Health Association Resource Study. Am J Respir Crit Care Med 189:408–418

Article  PubMed  PubMed Central  Google Scholar 

Hardin M, Foreman M, Dransfield MT et al (2016) Sex-specific features of emphysema among current and former smokers with COPD. Eur Respir J 47:104–112

Article  CAS  PubMed  Google Scholar 

Stockley RA, Parr DG, Piitulainen E, Stolk J, Stoel BC, Dirksen A (2010) Therapeutic efficacy of α-1 antitrypsin augmentation therapy on the loss of lung tissue: an integrated analysis of 2 randomised clinical trials using computed tomography densitometry. Respir Res 11:136

Article  PubMed  PubMed Central  Google Scholar 

Liu X (2012) Survival analysis: models and applications. Wiley, Hoboken

Comments (0)

No login
gif