  Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches
exist, involving sophisticated pipelines trained and validated on different
datasets. However, the clinical applicability of these approaches across
diseases remains limited. We compared four generic deep learning approaches
trained on various datasets and two readily available lung segmentation
algorithms. We performed evaluation on routine imaging data with more than six
different disease patterns and three published data sets. Using different deep
learning approaches, mean Dice similarity coefficients (DSCs) on test datasets
varied not over 0.02. When trained on a diverse routine dataset (n = 36) a
standard approach (U-net) yields a higher DSC (0.97 $\pm$ 0.05) compared to
training on public datasets such as Lung Tissue Research Consortium (0.94 $\pm$
0.13, p = 0.024) or Anatomy 3 (0.92 $\pm$ 0.15, p = 0.001). Trained on routine
data (n = 231) covering multiple diseases, U-net compared to reference methods
yields a DSC of 0.98 $\pm$ 0.03 versus 0.94 $\pm$ 0.12 (p = 0.024).
