Shape-Aware Complementary-Task Learning for Multi-organ Segmentation
Navarro F, Shit S, Ezhov I, Paetzold J, Gafita A, Peeken JC, Combs SE, Menze BH (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer
Book Volume: 11861 LNCS
Pages Range: 620-627
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030326913
DOI: 10.1007/978-3-030-32692-0_71
Abstract
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.
Involved external institutions
Germany (DE)How to cite
APA:
Navarro, F., Shit, S., Ezhov, I., Paetzold, J., Gafita, A., Peeken, J.C.,... Menze, B.H. (2019). Shape-Aware Complementary-Task Learning for Multi-organ Segmentation. In Heung-Il Suk, Mingxia Liu, Chunfeng Lian, Pingkun Yan (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 620-627). Shenzhen, CHN: Springer.
MLA:
Navarro, Fernando, et al. "Shape-Aware Complementary-Task Learning for Multi-organ Segmentation." Proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Heung-Il Suk, Mingxia Liu, Chunfeng Lian, Pingkun Yan, Springer, 2019. 620-627.
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