Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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F. Navarro, S. Shit, I. Ezhov, J. Paetzold, A. Gafita, J. Peeken, S. Combs, B. Menze
Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation
Proceedings of the 10th International Workshop on Machine Learning in Medical Imaging (MLMI 2019), Shenzhen, China, October 2019. (bib)

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.
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