  We introduce an accurate lung segmentation model for chest radiographs based
on deep convolutional neural networks. Our model is based on atrous
convolutional layers to increase the field-of-view of filters efficiently. To
improve segmentation performances further, we also propose a multi-stage
training strategy, network-wise training, which the current stage network is
fed with both input images and the outputs from pre-stage network. It is shown
that this strategy has an ability to reduce falsely predicted labels and
produce smooth boundaries of lung fields. We evaluate the proposed model on a
common benchmark dataset, JSRT, and achieve the state-of-the-art segmentation
performances with much fewer model parameters.
