Automatic multi-organ segmentation using learning-based segmentation and level set optimization

Authors:
Timo Kohlberger;Michal Sofka;Jingdan Zhang;Neil Birkbeck;Jens Wetzl;Jens Kaftan;Jérôme Declerck;S. Kevin Zhou
Affiliations:
Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ;Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ;Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ;Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ;Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ;Molecular Imaging, Siemens Healthcare, Oxford, UK;Molecular Imaging, Siemens Healthcare, Oxford, UK;Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

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Abstract

We present a novel generic segmentation system for the fully automatic multi-organ segmentation from CT medical images. Thereby we combine the advantages of learning-based approaches on point cloudbased shape representation, such a speed, robustness, point correspondences, with those of PDE-optimization-based level set approaches, such as high accuracy and the straightforward prevention of segment overlaps. In a benchmark on 10-100 annotated datasets for the liver, the lungs, and the kidneys we show that the proposed system yields segmentation accuracies of 1.17-2.89mm average surface errors. Thereby the level set segmentation (which is initialized by the learning-based segmentations) contributes with an 20%-40% increase in accuracy.