  Location information is proven to benefit the deep learning models on
capturing the manifold structure of target objects, and accordingly boosts the
accuracy of medical image segmentation. However, most existing methods encode
the location information in an implicit way, e.g. the distance transform maps,
which describe the relative distance from each pixel to the contour boundary,
for the network to learn. These implicit approaches do not fully exploit the
position information (i.e. absolute location) of targets. In this paper, we
propose a novel loss function, namely residual moment (RM) loss, to explicitly
embed the location information of segmentation targets during the training of
deep learning networks. Particularly, motivated by image moments, the
segmentation prediction map and ground-truth map are weighted by coordinate
information. Then our RM loss encourages the networks to maintain the
consistency between the two weighted maps, which promotes the segmentation
networks to easily locate the targets and extract manifold-structure-related
features. We validate the proposed RM loss by conducting extensive experiments
on two publicly available datasets, i.e., 2D optic cup and disk segmentation
and 3D left atrial segmentation. The experimental results demonstrate the
effectiveness of our RM loss, which significantly boosts the accuracy of
segmentation networks.
