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• W2213209872 abstract "We present an automatic segmentation method for the delineation and quantitative thickness measurement of multiple layers in endoscopic airway optical coherence tomography (OCT) images. The boundaries of the mucosa and the sub-mucosa layers are accurately extracted using a graph-theory-based dynamic programming algorithm. The algorithm was tested with sheep airway OCT images. Quantitative thicknesses of the mucosal layers are obtained automatically for smoke inhalation injury experiments." @default.
• W2213209872 created "2016-06-24" @default.
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• W2213209872 date "2015-12-24" @default.
• W2213209872 modified "2023-10-16" @default.
• W2213209872 title "Automatic airway wall segmentation and thickness measurement for long-range optical coherence tomography images" @default.
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• W2213209872 doi "https://doi.org/10.1364/oe.23.033992" @default.
• W2213209872 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4741311" @default.
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