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15 February 2021 Classification of lung disease using CT image encodings computed from a novel 3D segmentation U-Net
Prahlad G. Menon, Palash G. Shah
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Proceedings Volume 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications; 116010Z (2021) https://doi.org/10.1117/12.2582021
Event: SPIE Medical Imaging, 2021, Online Only
Abstract
The goal of this study is to demonstrate the classification value of the latent encodings of a neural network trained for image segmentation of the lung region. In order to achieve this, the gold standard of semantic segmentation, a 3D U-Net was used to extract the encodings for 20 thoracic CT images (10 COVID-19 and 10 Control), and a random forest classifier was trained based on the encodings developed from two training experiments. Performance was analyzed in terms of the independent classification value of each voxel of the U-Net’s latent encoding layer in distinguishing COVID-19 v/s control images.
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Prahlad G. Menon and Palash G. Shah "Classification of lung disease using CT image encodings computed from a novel 3D segmentation U-Net", Proc. SPIE 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 116010Z (15 February 2021); https://doi.org/10.1117/12.2582021
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KEYWORDS
Image segmentation
Computer programming
Image classification
Lung
Computed tomography
3D image processing
3D modeling
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