AeroPath: An airway segmentation benchmark dataset with challenging
  pathology
Karen-Helene Støverud David Bouget Andre Pedersen Håkon Olav Leira Thomas Langø Erlend Fagertun Hofstad
To improve the prognosis of patients suffering from pulmonary diseases, such
as lung cancer, early diagnosis and treatment are crucial. The analysis of CT
images is invaluable for diagnosis, whereas high quality segmentation of the
airway tree are required for intervention planning and live guidance during
bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22)
challenge released a large dataset, both enabling training of deep-learning
based models and bringing substantial improvement of the state-of-the-art for
the airway segmentation task. However, the ATM'22 dataset includes few patients
with severe pathologies affecting the airway tree anatomy. In this study, we
introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images
from patients with pathologies ranging from emphysema to large tumors, with
corresponding trachea and bronchi annotations. Second, we present a multiscale
fusion design for automatic airway segmentation. Models were trained on the
ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against
competitive open-source methods. The same performance metrics as used in the
ATM'22 challenge were used to benchmark the different considered approaches.
Lastly, an open web application is developed, to easily test the proposed model
on new data. The results demonstrated that our proposed architecture predicted
topologically correct segmentations for all the patients included in the
AeroPath dataset. The proposed method is robust and able to handle various
anomalies, down to at least the fifth airway generation. In addition, the
AeroPath dataset, featuring patients with challenging pathologies, will
contribute to development of new state-of-the-art methods. The AeroPath dataset
and the web application are made openly available.