  Recently, state-of-the-art results have been achieved in semantic
segmentation using fully convolutional networks (FCNs). Most of these networks
employ encoder-decoder style architecture similar to U-Net and are trained with
images and the corresponding segmentation maps as a pixel-wise classification
task. Such frameworks only exploit class information by using the ground truth
segmentation maps. In this paper, we propose a multi-task learning framework
with the main aim of exploiting structural and spatial information along with
the class information. We modify the decoder part of the FCN to exploit class
information and the structural information as well. We intend to do this while
also keeping the parameters of the network as low as possible. We obtain the
structural information using either of the two ways: i) using the contour map
and ii) using the distance map, both of which can be obtained from ground truth
segmentation maps with no additional annotation costs. We also explore
different ways in which distance maps can be computed and study the effects of
different distance maps on the segmentation performance. We also experiment
extensively on two different medical image segmentation applications: i.e i)
using color fundus images for optic disc and cup segmentation and ii) using
endoscopic images for polyp segmentation. Through our experiments, we report
results comparable to, and in some cases performing better than the current
state-of-the-art architectures and with an order of 2x reduction in the number
of parameters.
