  Accurate segmentation of anatomical structures in chest radiographs is
essential for many computer-aided diagnosis tasks. In this paper we investigate
the latest fully-convolutional architectures for the task of multi-class
segmentation of the lungs field, heart and clavicles in a chest radiograph. In
addition, we explore the influence of using different loss functions in the
training process of a neural network for semantic segmentation. We evaluate all
models on a common benchmark of 247 X-ray images from the JSRT database and
ground-truth segmentation masks from the SCR dataset. Our best performing
architecture, is a modified U-Net that benefits from pre-trained encoder
weights. This model outperformed the current state-of-the-art methods tested on
the same benchmark, with Jaccard overlap scores of 96.1% for lung fields, 90.6%
for heart and 85.5% for clavicles.
