  Convolutional neural networks (CNNs) have shown remarkable results over the
last several years for a wide range of computer vision tasks. A new
architecture recently introduced by Sabour et al., referred to as a capsule
networks with dynamic routing, has shown great initial results for digit
recognition and small image classification. The success of capsule networks
lies in their ability to preserve more information about the input by replacing
max-pooling layers with convolutional strides and dynamic routing, allowing for
preservation of part-whole relationships in the data. This preservation of the
input is demonstrated by reconstructing the input from the output capsule
vectors. Our work expands the use of capsule networks to the task of object
segmentation for the first time in the literature. We extend the idea of
convolutional capsules with locally-connected routing and propose the concept
of deconvolutional capsules. Further, we extend the masked reconstruction to
reconstruct the positive input class. The proposed
convolutional-deconvolutional capsule network, called SegCaps, shows strong
results for the task of object segmentation with substantial decrease in
parameter space. As an example application, we applied the proposed SegCaps to
segment pathological lungs from low dose CT scans and compared its accuracy and
efficiency with other U-Net-based architectures. SegCaps is able to handle
large image sizes (512 x 512) as opposed to baseline capsules (typically less
than 32 x 32). The proposed SegCaps reduced the number of parameters of U-Net
architecture by 95.4% while still providing a better segmentation accuracy.
