Registration-Invariant Biomechanical Features for Disease Staging of COPD in SPIROMICS

[1]  Eric A. Hoffman,et al.  Assessment of Lung Biomechanics in COPD Using Image Registration , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[2]  David W. Kaczka,et al.  Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species , 2019, Medical Image Anal..

[3]  J. Wedzicha,et al.  Update on Clinical Aspects of Chronic Obstructive Pulmonary Disease. , 2019, The New England journal of medicine.

[4]  Wei Shao Improving functional avoidance radiation therapy by image registration , 2019 .

[5]  Michael Eberlein,et al.  Predictive value of prebronchodilator and postbronchodilator spirometry for COPD features and outcomes , 2017, BMJ Open Respiratory Research.

[6]  Andriy Fedorov,et al.  Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.

[7]  Eric A Hoffman,et al.  Biomechanical CT metrics are associated with patient outcomes in COPD , 2017, Thorax.

[8]  Zhongheng Zhang,et al.  Variable selection with stepwise and best subset approaches. , 2016, Annals of translational medicine.

[9]  Guido Gerig,et al.  Morphometry of anatomical shape complexes with dense deformations and sparse parameters , 2014, NeuroImage.

[10]  A HoffmanEric,et al.  COPD研究(SPIROMICS)における亜集団と中間結果のデザイン , 2014 .

[11]  Lisa M LaVange,et al.  Design of the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS) , 2013, Thorax.

[12]  Eric A Hoffman,et al.  Registration-based lung mechanical analysis of chronic obstructive pulmonary disease (COPD) using a supervised machine learning framework. , 2013, Academic radiology.

[13]  Alain Trouvé,et al.  The Varifold Representation of Nonoriented Shapes for Diffeomorphic Registration , 2013, SIAM J. Imaging Sci..

[14]  F. Martinez,et al.  Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. , 2007, American journal of respiratory and critical care medicine.

[15]  Ella A. Kazerooni,et al.  CT-based Biomarker Provides Unique Signature for Diagnosis of COPD Phenotypes and Disease Progression , 2012, Nature Medicine.

[16]  G. Christensen,et al.  Three-dimensional characterization of regional lung deformation. , 2011, Journal of biomechanics.

[17]  E. Regan,et al.  Genetic Epidemiology of COPD (COPDGene) Study Design , 2011, COPD.

[18]  E. Hoffman,et al.  A multivariate analysis of risk factors for the air-trapping asthmatic phenotype as measured by quantitative CT analysis. , 2009, Chest.

[19]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[20]  G. Ruxton The unequal variance t-test is an underused alternative to Student's t-test and the Mann–Whitney U test , 2006 .

[21]  Jeremy N. V. Miles,et al.  R Squared, Adjusted R Squared† , 2005 .

[22]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[23]  James G. Surles,et al.  Model-Dependent Variance Inflation Factor Cutoff Values , 2002 .

[24]  D. Lynch,et al.  Quantitative computed tomography detects air trapping due to asthma. , 1994, Chest.

[25]  R. Coifman,et al.  Fast wavelet transforms and numerical algorithms I , 1991 .

[26]  N. Müller,et al.  "Density mask". An objective method to quantitate emphysema using computed tomography. , 1988, Chest.

[27]  David L. Wallace,et al.  Simplified Beta-Approximations to the Kruskal-Wallis H Test , 1959 .