title: Automatic multi-organ segmentation using learning-based segmentation and level set optimization.
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 cloud-based 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 1.17-2.89 mm average surface errors. Thereby the level set segmentation (which is initialized by the learning-based segmentations) contributes with an 20%-40% increase in accuracy.
authors: Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA.
authors: Timo
authors: T
authors: Kohlberger
authors: Michal
authors: M
authors: Sofka
authors: Jingdan
authors: J
authors: Zhang
authors: Neil
authors: N
authors: Birkbeck
authors: Jens
authors: J
authors: Wetzl
authors: Jens
authors: J
authors: Kaftan
authors: Jérôme
authors: J
authors: Declerck
authors: S Kevin
authors: SK
authors: Zhou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007668:Kidney / Q000473:pathology; D007858:Learning; D008099:Liver / Q000473:pathology; D008168:Lung / Q000473:pathology; D008953:Models, Anatomic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*