  Image segmentation is a primary task in many medical applications. Recently,
many deep networks derived from U-Net have been extensively used in various
medical image segmentation tasks. However, in most of the cases, networks
similar to U-net produce coarse and non-smooth segmentations with lots of
discontinuities. To improve and refine the performance of U-Net like networks,
we propose the use of parallel decoders which along with performing the mask
predictions also perform contour prediction and distance map estimation. The
contour and distance map aid in ensuring smoothness in the segmentation
predictions. To facilitate joint training of three tasks, we propose a novel
architecture called Psi-Net with a single encoder and three parallel decoders
(thus having a shape of $\Psi$), one decoder to learns the segmentation mask
prediction and other two decoders to learn the auxiliary tasks of contour
detection and distance map estimation. The learning of these auxiliary tasks
helps in capturing the shape and the boundary information. We also propose a
new joint loss function for the proposed architecture. The loss function
consists of a weighted combination of Negative Log likelihood and Mean Square
Error loss. We have used two publicly available datasets: 1) Origa dataset for
the task of optic cup and disc segmentation and 2) Endovis segment dataset for
the task of polyp segmentation to evaluate our model. We have conducted
extensive experiments using our network to show our model gives better results
in terms of segmentation, boundary and shape metrics.
