Dr. Miranda Kirby Profile

Dr. Miranda Kirby

Postdoc Research Fellow at Univ of British Columbia

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Publications (8)

Proceedings Article | 2 April 2024 Presentation + Paper

Classifying chronic obstructive pulmonary disease using computed tomography imaging and 2D and 3D convolutional neural networks

S. Peterson, S. Rezvanjou, A. Moslemi, W. C. Tan, J. Hogg, J. Bourbeau, M. Kirby

Proceedings Volume 12930, 129300M (2024) https://doi.org/10.1117/12.3006852

KEYWORDS: Chronic obstructive pulmonary disease, Lung, 3D image processing, Convolutional neural networks

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Convolutional Neural Network (CNN)-based models using Computed Tomography (CT) images classify Chronic Obstructive Pulmonary Disease (COPD) patients with high accuracy, but studies have used various different input images and it is unclear what input images are optimum, particularly in a milder COPD cohort. We propose a novel approach using 2D airway-optimized topological multi-planar reformat (airway-optimized tMPR) images as well as novel 3D fusion methods and compared the performance of these models with various established 2D/3D CNN-based methods in a population-based mild COPD cohort. Participants from the CanCOLD study were evaluated. We implemented several 2D/3D models adapted from the literature. Existing CNN-based models were trained using 2D collages of axial/coronal/sagittal slices, and colored and binary airway images. 3D models consisting of 15 axial inspiratory/expiratory slices were selected, and input and output combination methods were investigated. For the proposed models, 2D airway-optimized tMPR images were constructed using cut-surface renderings to convey shape and interior/contextual information. 3D output fusion of axial/coronal/sagittal images, as well as output fusion of the axial and 3D airway tree, were also investigated. Finally, the output fusion of 2D airway-optimized tMPR methods and 3D lungs combined method was investigated. 742 participants were used for training/validation and 309 for testing. The 2D and 3D methods adapted from the literature had accuracy ranging from 61%-72% in the mild COPD cohort. The 2D airway-optimized tMPR model achieved 73% accuracy. The proposed 3D model of combining axial/coronal/sagittal images had an accuracy of 75%. The proposed model output combining 2D colored airways and inspiratory combined 3D images, and the 3D collage of axial/coronal/sagittal images, resulted in 74% and 73% accuracy, respectively. However, the output fusion of the airway-optimized tMPR and 3D lung model of combining axial/coronal/sagittal images reached the highest accuracy of 78%. While the CNN model with 2D airway/lung-optimized images had improved performance with reduced computational resources as compared to the 3D models proposed, as well as the other published CNN-based models, the combination of this 2D method with the 3D CNN model of combining axial/coronal/sagittal images achieved the highest performance in this mild cohort.

Proceedings Article | 15 February 2021 Presentation + Paper

Theoretical optimization of dual-energy x-ray imaging of chronic obstructive pulmonary disease (COPD)

Fateen Basharat, Michael Belli, Miranda Kirby, Jesse Tanguay

Proceedings Volume 11595, 1159526 (2021) https://doi.org/10.1117/12.2580455

KEYWORDS: Chronic obstructive pulmonary disease, X-ray imaging, X-rays, Radiography, Quantum efficiency, Defect detection, Structural imaging, Spatial resolution, Lung, Image quality standards

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Proceedings Article | 1 March 2019 Presentation + Paper

Theoretical feasibility of dual-energy functional x-ray imaging of respiratory disease

Jesse Tanguay, Miranda Kirby

Proceedings Volume 10948, 109481D (2019) https://doi.org/10.1117/12.2512519

KEYWORDS: Signal to noise ratio, Xenon, Lung, Pulmonary disorders, Bone, X-rays, X-ray imaging, Dual energy imaging, Functional imaging, Cystic fibrosis, Chronic obstructive pulmonary disease

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SPIE Journal Paper | 28 June 2018

Development of a pulmonary imaging biomarker pipeline for phenotyping of chronic lung disease

Fumin Guo, Dante Capaldi, Miranda Kirby, Khadija Sheikh, Sarah Svenningsen, David McCormack, Aaron Fenster, Grace Parraga

JMI, Vol. 5, Issue 02, 026002, (June 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.2.026002

KEYWORDS: Magnetic resonance imaging, Lung, Image segmentation, Computed tomography, Software development, Point-of-care devices, Visualization, Algorithm development, Xenon, Image analysis

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Proceedings Article | 4 March 2014 Paper

Automated segmentation of porcine airway wall layers using optical coherence tomography: comparison with manual segmentation and histology

Miranda Kirby, Anthony Lee, Tara Candido, Calum MacAulay, Pierre Lane, Stephen Lam, Harvey Coxson

Proceedings Volume 8927, 89271D (2014) https://doi.org/10.1117/12.2040866

KEYWORDS: Optical coherence tomography, Image segmentation, Image filtering, Cartilage, In vivo imaging, Lung, Computed tomography, Pulmonary disorders, Image acquisition, Nonlinear filtering

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Proceedings Article | 4 March 2014 Paper

Sex differences in chronic obstructive pulmonary disease evaluated using optical coherence tomography

Miranda Kirby, Wei Zhang, Peter Laratta, Don Sin, Stephen Lam, Harvey Coxson

Proceedings Volume 8927, 89270Z (2014) https://doi.org/10.1117/12.2040824

KEYWORDS: Optical coherence tomography, Chronic obstructive pulmonary disease, Lung, Pathophysiology, Spirometry, Adaptive optics, Image resolution, Computed tomography, Pathogens, Imaging systems

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Proceedings Article | 6 April 2012 Paper

Two and three-dimensional segmentation of hyperpolarized 3He magnetic resonance imaging of pulmonary gas distribution

Andrew Wheatley, Miranda Kirby, Aaron Fenster, Grace Parraga, Mohammadreza Heydarian

Proceedings Volume 8317, 83171C (2012) https://doi.org/10.1117/12.910907

KEYWORDS: Image segmentation, Magnetic resonance imaging, Lung, 3D metrology, Chronic obstructive pulmonary disease, 3D image processing, 3D acquisition, Algorithm development, Image registration, Image processing algorithms and systems

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Proceedings Article | 9 March 2010 Paper

Development and application of methods to quantify spatial and temporal hyperpolarized 3He MRI ventilation dynamics: preliminary results in chronic obstructive pulmonary disease

Andrew Wheatley, Grace Parraga, Miranda Kirby, David McCormack

Proceedings Volume 7626, 762605 (2010) https://doi.org/10.1117/12.843821

KEYWORDS: Image segmentation, Magnetic resonance imaging, Chronic obstructive pulmonary disease, Image registration, Image analysis, Lung, Statistical analysis, Medical research, Medicine, Medical imaging

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Showing 5 of 8 publications

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