  Analysis of cancer and other pathological diseases, like the interstitial
lung diseases (ILDs), is usually possible through Computed Tomography (CT)
scans. To aid this, a preprocessing step of segmentation is performed to reduce
the area to be analyzed, segmenting the lungs and removing unimportant regions.
Generally, complex methods are developed to extract the lung region, also using
hand-made feature extractors to enhance segmentation. With the popularity of
deep learning techniques and its automated feature learning, we propose a lung
segmentation approach using fully convolutional networks (FCNs) combined with
fully connected conditional random fields (CRF), employed in many
state-of-the-art segmentation works. Aiming to develop a generalized approach,
the publicly available datasets from University Hospitals of Geneva (HUG) and
VESSEL12 challenge were studied, including many healthy and pathological CT
scans for evaluation. Experiments using the dataset individually, its trained
model on the other dataset and a combination of both datasets were employed.
Dice scores of $98.67\%\pm0.94\%$ for the HUG-ILD dataset and
$99.19\%\pm0.37\%$ for the VESSEL12 dataset were achieved, outperforming works
in the former and obtaining similar state-of-the-art results in the latter
dataset, showing the capability in using deep learning approaches.
