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7 March 2022 Application of U-Net convolutional neural network in evaluating pig airway volume for the assessment of acute respiratory distress syndrome (ARDS) using optical coherence tomography (OCT)
Raksha Sreeramachandra Murthy, Li-Dek Chou, Andriy I. Batchinsky, Zhongping Chen
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Proceedings Volume 11937, Endoscopic Microscopy XVII; 1193704 (2022) https://doi.org/10.1117/12.2610666
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
Acute respiratory distress syndrome (ARDS) is a form of lung injury that is associated with inflammation and increased permeability in the lung. It is characterized by acute arterial hypoxemia. The accurate assessment of the airway damage due to smoke inhalation injury (SII) plays a vital role in facilitating appropriate treatment strategies and improved clinical outcomes. This study evaluates the efficiency and accuracy of a trained neural network in segmenting the pig airway images which is used in the assessment of ARDS caused by smoke inhalation injury (SII). The neural network is modeled after the U-net convolutional neural network and the segmentation accuracy is calculated.
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Raksha Sreeramachandra Murthy, Li-Dek Chou, Andriy I. Batchinsky, and Zhongping Chen "Application of U-Net convolutional neural network in evaluating pig airway volume for the assessment of acute respiratory distress syndrome (ARDS) using optical coherence tomography (OCT)", Proc. SPIE 11937, Endoscopic Microscopy XVII, 1193704 (7 March 2022); https://doi.org/10.1117/12.2610666
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KEYWORDS
Optical coherence tomography
Convolutional neural networks
Image segmentation
Neural networks
Injuries
Biomedical optics
Computer programming
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