ArticleMedical physics2021
A pilot study: Quantify lung volume and emphysema extent directly from two-dimensional scout images.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Simulation-Driven Annotation-Free Deep Learning for Automated Detection and Segmentation of Airway Mucus Plugs on Non-Contrast CT Images.Bioengineering (Basel, Switzerland) · 2026Article
- Imaging Phenotypes Assessment by Using Quantitative Parameters for CT-Defined Subtypes of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2025Article
- XRayWizard: Reconstructing 3-D lung surfaces from a single 2-D chest x-ray image via Vision Transformer.Medical physics · 2024Article
- Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning.Scientific reports · 2021Article
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Abstract
purposeThe potential to compute volume metrics of emphysema from planar scout images was investigated in this study. The successful implementation of this concept will have a wide impact in different fields, and specifically, maximize the diagnostic potential of the planar medical images.
methodsWe investigated our premise using a well-characterized chronic obstructive pulmonary disease (COPD) cohort. In this cohort, planar scout images from computed tomography (CT) scans were used to compute lung volume and percentage of emphysema. Lung volume and percentage of emphysema were quantified on the volumetric CT images and used as the "ground truth" for developing the models to compute the variables from the corresponding scout images. We trained two classical convolutional neural networks (CNNs), including VGG19 and InceptionV3, to compute lung volume and the percentage of emphysema from the scout images. The scout images (n = 1,446) were split into three subgroups: (1) training (n = 1,235), (2) internal validation (n = 99), and (3) independent test (n = 112) at the subject level in a ratio of 8:1:1. The mean absolute difference (MAD) and R-square (R2) were the performance metrics to evaluate the prediction performance of the developed models.
resultsThe lung volumes and percentages of emphysema computed from a single planar scout image were significantly linear correlated with the measures quantified using volumetric CT images (VGG19: R2 = 0.934 for lung volume and R2 = 0.751 for emphysema percentage, and InceptionV3: R2 = 0.977 for lung volume and R2 = 0.775 for emphysema percentage). The mean absolute differences (MADs) for lung volume and percentage of emphysema were 0.302 ± 0.247L and 2.89 ± 2.58%, respectively, for VGG19, and 0.366 ± 0.287L and 3.19 ± 2.14, respectively, for InceptionV3.
conclusionsOur promising results demonstrated the feasibility of inferring volume metrics from planar images using CNNs.
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