title: Liver segmentation using sparse 3D prior models with optimal data support.
abstract: Volume segmentation is a relatively slow process and, in certain circumstances, the enormous amount of prior knowledge available is underused. Model-based liver segmentation suffers from the large shape variability of this organ, and from structures of similar appearance that juxtapose the liver. The technique presented in this paper is devoted to combine a statistical analysis of the data with a reconstruction model from sparse information: only the most reliable information in the image is used, and the rest of the liver's shape is inferred from the model and the sparse observation. The resulting process is more efficient than standard segmentation since most of the workload is concentrated on the critical points, but also more robust, since the interpolated volume is consistent with the prior knowledge statistics. The experimental results on liver datasets prove the sparse information model has the same potential as PCA, if not better, to represent the shape of the liver. Furthermore, the performance assessment from measurement statistics on the liver's volume, distance between reconstructed surfaces and ground truth, and inter-observer variability demonstrates the liver is efficiently segmented using sparse information.
authors.affiliation: Imaging & Visualization Department, Siemens Corporate Research, Princeton, NJ, USA.
authors.forename: Charles
authors.initials: C
authors.lastname: Florin
authors.forename: Nikos
authors.initials: N
authors.lastname: Paragios
authors.forename: Gareth
authors.initials: G
authors.lastname: Funka-Lea
authors.forename: James
authors.initials: J
authors.lastname: Williams
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008099:Liver / Q000000981:diagnostic imaging*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*