  In this work, we propose to resolve the issue existing in current deep
learning based organ segmentation systems that they often produce results that
do not capture the overall shape of the target organ and often lack smoothness.
Since there is a rigorous mapping between the Signed Distance Map (SDM)
calculated from object boundary contours and the binary segmentation map, we
exploit the feasibility of learning the SDM directly from medical scans. By
converting the segmentation task into predicting an SDM, we show that our
proposed method retains superior segmentation performance and has better
smoothness and continuity in shape. To leverage the complementary information
in traditional segmentation training, we introduce an approximated Heaviside
function to train the model by predicting SDMs and segmentation maps
simultaneously. We validate our proposed models by conducting extensive
experiments on a hippocampus segmentation dataset and the public MICCAI 2015
Head and Neck Auto Segmentation Challenge dataset with multiple organs. While
our carefully designed backbone 3D segmentation network improves the Dice
coefficient by more than 5% compared to current state-of-the-arts, the proposed
model with SDM learning produces smoother segmentation results with smaller
Hausdorff distance and average surface distance, thus proving the effectiveness
of our method.
