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Automating bronchoconstriction analysis based on U-Net

[Industrial and application paper]
: Steinmeyer, C.; Dehmel, S.; Theidel, D.; Braun, A.; Wiese, L.

Fulltext ()

Costa, C.:
Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2021. Online resource : Proceedings of the Workshops of the EDBT/ICDT 2021 Joint Conference. Nicosia, Cyprus, March 23, 2021
Nikosia: CEUR, 2021 (CEUR Workshop Proceedings 2841)
ISSN: 1613-0073
6 pp.
International Conference on Extending Database Technology (EDBT) <24, 2021, Online>
International Conference on Database Theory (ICDT) <24, 2021, Online>
Conference Paper, Electronic Publication
Fraunhofer ITEM ()

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.