  Multi-organ segmentation in whole-body computed tomography (CT) is a constant
pre-processing step which finds its application in organ-specific image
retrieval, radiotherapy planning, and interventional image analysis. We address
this problem from an organ-specific shape-prior learning perspective. We
introduce the idea of complementary-task learning to enforce shape-prior
leveraging the existing target labels. We propose two complementary-tasks
namely i) distance map regression and ii) contour map detection to explicitly
encode the geometric properties of each organ. We evaluate the proposed
solution on the public VISCERAL dataset containing CT scans of multiple organs.
We report a significant improvement of overall dice score from 0.8849 to 0.9018
due to the incorporation of complementary-task learning.
