title: A label fusion method using conditional random fields with higher-order potentials: Application to hippocampal segmentation.
abstract: OBJECTIVE
  The objective of this study is to develop a probabilistic modeling framework for segmenting structures of interest from a collection of atlases. We present a label fusion method that is based on minimizing an energy function using graph-cut techniques.
  
  
  METHODS
  We use a conditional random field (CRF) model that allows us to efficiently incorporate shape, appearance and context information. This model is characterized by a pseudo-Boolean function defined on unary, pairwise and higher-order potentials. Given a subset of registered atlases in the target image for a particular region of interest (ROI), we first derive an appearance-shape model from these registered atlases. The unary potentials combine an appearance model based on multiple features with a label prior using a weighted voting method. The pairwise terms are defined from a Finsler metric that minimizes the surface of separation between voxels whose labels are different. The higher-order potentials used in our framework are based on the robust P(n) model proposed by Kohli et al. The higher-order potentials enforce label consistency in cliques; hence, the proposed method can be viewed as an approach to integrate high-level information with images based on low-level features. To evaluate the performance and the robustness of the proposed label fusion method, we employ two available databases of T1-weighted (T1W) magnetic resonance (MR) images of human brains. We compare our approach with other label fusion methods in the automatic hippocampal segmentation from T1W-MR images.
  
  
  RESULTS
  Our label fusion method yields mean Dice coefficients of 0.829 and 0.790 for the two databases used with mean times of approximately 80 and 160s, respectively.
  
  
  CONCLUSIONS
  We introduce a new label fusion method based on a CRF model and on ROIs. The CRF model is characterized by a pseudo-Boolean function defined on unary, pairwise and higher-order potentials. The proposed Boolean function is representable by graphs. A globally optimal binary labeling is found using a st-mincut algorithm in each ROI. We show that the proposed approach is very competitive with respect to recently reported methods.
authors.affiliation: Health Science Technology Group, Technical University of Madrid, Ronda de Valencia 3, 28012 Madrid, Spain. Electronic address: carlos.platero@upm.es.
authors.forename: Carlos
authors.initials: C
authors.lastname: Platero
authors.affiliation: Health Science Technology Group, Technical University of Madrid, Ronda de Valencia 3, 28012 Madrid, Spain.
authors.forename: M
authors.initials: M
authors.lastname: Carmen Tobar
mesh_terms: D000465:Algorithms; D001271:Atlases as Topic; D001331:Automation; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D006624:Hippocampus / Q000033:anatomy & histology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D011336:Probability; D015203:Reproducibility of Results
keywords: Atlas-based segmentation; Global optimization; Graph cuts; Hippocampal segmentation; Image registration; Label fusion; Magnetic resonance imaging