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2021
Conference Paper
Titel
Automating bronchoconstriction analysis based on U-Net
Titel Supplements
[Industrial and application paper]
Abstract
Advances in deep learning enable the automation of a multitude of image analysis tasks. Yet, many solutions still rely on less automated, less advanced processes. To transition from an existing solution to a deep learning based one, an appropriate dataset needs to be created, preprocessed, as well as a model needs to be developed, and trained on these data. We successfully employ this process for bronchoconstriction analysis in Precision Cut Lung Slices for pre-clinical drug research. Our automated approach uses a variant of U-net for the core task of airway segmentation and reaches (mean) Intersection over Union of 0.9. It performs comparably to the semi-manual previous approach, but is approximately 80 times faster.