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7 March 2022 Deep learning-based automated, high-throughput microscopic assessment of interstitial lung disease using endobronchial optical coherence tomography
Sreyankar Nandy, Sarita R. Berigei, Benjamin W. Roop, Melissa J. Suter, Markus D. Herrmann, Lida P. Hariri
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Proceedings Volume PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI; PC119481E (2022) https://doi.org/10.1117/12.2612956
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
Idiopathic pulmonary fibrosis (IPF) is a fatal form of fibrotic interstitial lung disease (ILD). Early diagnosis of IPF is essential, however, resolution limitations of HRCT prohibit identification and monitoring of early microanatomic alterations. Developing precise imaging biomarkers using quantitative imaging features and artificial intelligence has significant potential for early diagnosis of IPF and non IPF ILDs, as well as for monitoring disease progression and therapeutic response. We demonstrate the feasibility of a deep learning-based algorithm for accurate segmentation and classification of salient microscopic ILD imaging features on endobronchial optical coherence tomography (EB-OCT) imaging.
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Sreyankar Nandy, Sarita R. Berigei, Benjamin W. Roop, Melissa J. Suter, Markus D. Herrmann, and Lida P. Hariri "Deep learning-based automated, high-throughput microscopic assessment of interstitial lung disease using endobronchial optical coherence tomography", Proc. SPIE PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, PC119481E (7 March 2022); https://doi.org/10.1117/12.2612956
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KEYWORDS
Lung
Optical coherence tomography
Algorithm development
Image segmentation
Emphysema
Evolutionary algorithms
Image classification
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