title: Low-rank to the rescue - atlas-based analyses in the presence of pathologies.
abstract: Low-rank image decomposition has the potential to address a broad range of challenges that routinely occur in clinical practice. Its novelty and utility in the context of atlas-based analysis stems from its ability to handle images containing large pathologies and large deformations. Potential applications include atlas-based tissue segmentation and unbiased atlas building from data containing pathologies. In this paper we present atlas-based tissue segmentation of MRI from patients with large pathologies. Specifically, a healthy brain atlas is registered with the low-rank components from the input MRIs, the low-rank components are then re-computed based on those registrations, and the process is then iteratively repeated. Preliminary evaluations are conducted using the brain tumor segmentation challenge data (BRATS '12).
authors.forename: Xiaoxiao
authors.initials: X
authors.lastname: Liu
authors.forename: Marc
authors.initials: M
authors.lastname: Niethammer
authors.forename: Roland
authors.initials: R
authors.lastname: Kwitt
authors.forename: Matthew
authors.initials: M
authors.lastname: McCormick
authors.forename: Stephen
authors.initials: S
authors.lastname: Aylward
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000473:pathology*; D003198:Computer Simulation; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique