0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      Preprint
      , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Related collections

          Author and article information

          Journal
          29 September 2022
          Article
          2209.15094
          8342a275-106b-43b3-9a88-a316f5d98e93

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          Custom metadata
          Open source code and a pip package for predictions with our model and trained weights are in https://github.com/MICLab-Unicamp/medseg
          eess.IV cs.CV

          Computer vision & Pattern recognition,Electrical engineering
          Computer vision & Pattern recognition, Electrical engineering

          Comments

          Comment on this article