Airway segmentation in computed tomography images can be used to analyze pulmonary diseases, however, manual segmentation is labor intensive and relies on expert knowledge. This manuscript details our contribution to MICCAI's 2022 Airway Tree Modelling challenge, a competition of fully automated methods for airway segmentation. We employed a previously developed deep learning architecture based on a modified EfficientDet (MEDSeg), training from scratch for binary airway segmentation using the provided annotations. Our method achieved 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 designed or tuned for airway segmentation. Open source code and a pip package for predictions with our model and trained weights are in https://github.com/MICLab-Unicamp/medseg.
@article{arxiv.2209.15094,
title = {Open-source tool for Airway Segmentation in Computed Tomography using 2.5D Modified EfficientDet: Contribution to the ATM22 Challenge},
author = {Diedre Carmo and Leticia Rittner and Roberto Lotufo},
journal= {arXiv preprint arXiv:2209.15094},
year = {2022}
}
Comments
Open source code, graphical user interface, and a pip package for predictions with our model and trained weights are in https://github.com/MICLab-Unicamp/medseg