  Hourglass networks such as the U-Net and V-Net are popular neural
architectures for medical image segmentation and counting problems. Typical
instances of hourglass networks contain shortcut connections between mirroring
layers. These shortcut connections improve the performance and it is
hypothesized that this is due to mitigating effects on the vanishing gradient
problem and the ability of the model to combine feature maps from earlier and
later layers. We propose a method for not only combining feature maps of
mirroring layers but also feature maps of layers with different spatial
dimensions. For instance, the method enables the integration of the bottleneck
feature map with those of the reconstruction layers. The proposed approach is
applicable to any hourglass architecture. We evaluated the contextual hourglass
networks on image segmentation and object counting problems in the medical
domain. We achieve competitive results outperforming popular hourglass networks
by up to 17 percentage points.
