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Airway segmentation in computed tomography images can be used to analyzepulmonary diseases, however, manual segmentation is labor intensive and relieson expert knowledge. This manuscript details our contribution to MICCAI's 2022Airway Tree Modelling challenge, a competition of fully automated methods forairway segmentation. We employed a previously developed deep learningarchitecture based on a modified EfficientDet (MEDSeg), training from scratchfor binary airway segmentation using the provided annotations. Our methodachieved 90.72 Dice in internal validation, 95.52 Dice on external validation,and 93.49 Dice in the final test phase, while not being specifically designedor tuned for airway segmentation. Open source code and a pip package forpredictions with our model and trained weights are inhttps://github.com/MICLab-Unicamp/medseg.

Open-source tool for Airway Segmentation in Computed Tomography using 2.5D Modified EfficientDet: Contribution to the ATM22 Challenge