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.

本研究旨在基于器官特异性形态先验学习的角度来解决全身 CT 扫描中的多器官分割问题，提出了两项互补任务 —— 距离图回归和轮廓图检测 —— 以明确编码每个器官的几何特性，并在公共数据集上评估所提出的解决方案，对比实验结果表明，互补任务学习的加入在 Dice 评分方面达到了显著改进.