Automatizing the Deep Learning pipeline for Out-of-Distribution Lung Segmentation

Data and models used in the paper "Automatizing the Deep Learning pipeline for Out-of-Distribution Lung Segmentation”, by A. Gómez, M. Buccardi, N. Miguel-Ramiro, ML. Soto-Montenegro, F. F. Stellari, M. Desco and M. Abella submitted to Scientific Reports, corresponding to the database SSA. 

data.zip: Database consisting of 58 C57bl/6 rodent studies with varying levels of acute lung injury and their corresponding manual lungs segmentations. Input volumes are 512×512×576 voxels with an isotropic voxel size of 0.122 mm, whereas the voxel size for labels is doubled to 0.244 mm. The SSA folder must be dragged onto the "data" folder in AutoLung to run succesfully.

The TCD and IST databases can be accessed through https://doi.org/10.6084/m9.figshare.c.4224377 and the LUNA database can be accessed through https://luna16.grand-challenge.org/Data/. The QGX2 database is proprietary; to request access, please contact [e-mail].

models.zip: Models trained on the joint SSA, IST and TCD dabasases, leaving 15% of the volumes for validation. The "localization" and "segmentation" folders, containing five pretrained models, must be dragged onto the "models" folder in AutoLung to run succesfully.

The code necessary to reproduce these experiments, as well as a user manual for the segmentation interface, is available under the GPL-3.0 license at https://github.com/BiiGXray/AutoLung.