title: Hierarchical max-flow segmentation framework for multi-atlas segmentation with Kohonen self-organizing map based Gaussian mixture modeling.
abstract: The incorporation of intensity, spatial, and topological information into large-scale multi-region segmentation has been a topic of ongoing research in medical image analysis. Multi-region segmentation problems, such as segmentation of brain structures, pose unique challenges in image segmentation in which regions may not have a defined intensity, spatial, or topological distinction, but rely on a combination of the three. We propose a novel framework within the Advanced segmentation tools (ASETS)(2), which combines large-scale Gaussian mixture models trained via Kohonen self-organizing maps, with deformable registration, and a convex max-flow optimization algorithm incorporating region topology as a hierarchy or tree. Our framework is validated on two publicly available neuroimaging datasets, the OASIS and MRBrainS13 databases, against the more conventional Potts model, achieving more accurate segmentations. Each component is accelerated using general-purpose programming on graphics processing Units to ensure computational feasibility.
authors.affiliation: Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada. Electronic address: mrajchl@robarts.ca.
authors.forename: Martin
authors.initials: M
authors.lastname: Rajchl
authors.affiliation: Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada. Electronic address: jbaxter@robarts.ca.
authors.forename: John S H
authors.initials: JS
authors.lastname: Baxter
authors.affiliation: Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada.
authors.forename: A Jonathan
authors.initials: AJ
authors.lastname: McLeod
authors.affiliation: Robarts Research Institute, Western University, London, ON, Canada.
authors.forename: Jing
authors.initials: J
authors.lastname: Yuan
authors.affiliation: Robarts Research Institute, Western University, London, ON, Canada.
authors.forename: Wu
authors.initials: W
authors.lastname: Qiu
authors.affiliation: Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada.
authors.forename: Terry M
authors.initials: TM
authors.lastname: Peters
authors.affiliation: Robarts Research Institute, Western University, London, ON, Canada.
authors.forename: Ali R
authors.initials: AR
authors.lastname: Khan
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: ASETS; Convex optimization; GPGPU; Kohonen self-organizing map; Multi-region segmentation