Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation
This work addresses a key pre-processing step for applications like radiotherapy planning, but it is incremental as it builds on existing methods with specific geometric enhancements.
The paper tackled multi-organ segmentation in whole-body CT scans by introducing complementary-task learning with distance map regression and contour map detection to enforce shape priors, resulting in an improvement of the overall dice score from 0.8849 to 0.9018.
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.