title: Unsupervised tissue segmentation from dynamic contrast-enhanced magnetic resonance imaging.
abstract: OBJECTIVE
  Design, implement, and validate an unsupervised method for tissue segmentation from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
  
  
  METHODS
  For each DCE-MRI acquisition, after a spatial registration phase, the time-varying intensity of each voxel is represented as a sparse linear combination of adaptive basis signals. Both the basis signals and the sparse coefficients are learned by minimizing a functional consisting of a data fidelity term and a sparsity inducing penalty. Tissue segmentation is then obtained by applying a standard clustering algorithm to the computed representation.
  
  
  RESULTS
  Quantitative estimates on two real data sets are presented. In the first case, the overlap with expert annotation measured with the DICE metric is nearly 90% and thus 5% more accurate than state-of-the-art techniques. In the second case, assessment of the correlation between quantitative scores, obtained by the proposed method against imagery manually annotated by two experts, achieved a Pearson coefficient of 0.83 and 0.87, and a Spearman coefficient of 0.83 and 0.71, respectively.
  
  
  CONCLUSIONS
  The sparse representation of DCE MRI signals obtained by means of adaptive dictionary learning techniques appears to be well-suited for unsupervised tissue segmentation and applicable to different clinical contexts with little effort.
authors.affiliation: Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Università degli Studi di Genova, Via Dodecaneso 35, 16146 Genova, Italy. Electronic address: gabriele.chiusano@unige.it.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Chiusano
authors.affiliation: Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Università degli Studi di Genova, Via Dodecaneso 35, 16146 Genova, Italy. Electronic address: alessandra.stagliano@unige.it.
authors.forename: Alessandra
authors.initials: A
authors.lastname: Staglianò
authors.affiliation: CAMELOT Srl, Via Greto di Cornigliano 6R, 16152 Genova, Italy. Electronic address: curzio.basso@camelotbio.com.
authors.forename: Curzio
authors.initials: C
authors.lastname: Basso
authors.affiliation: Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Università degli Studi di Genova, Via Dodecaneso 35, 16146 Genova, Italy. Electronic address: alessandro.verri@unige.it.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Verri
mesh_terms: D001185:Artificial Intelligence; D003287:Contrast Media; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D013583:Synovial Membrane / Q000473:pathology; D014955:Wrist Joint / Q000473:pathology
chemical_list: D003287:Contrast Media
keywords: Dictionary learning; Dynamic contrast enhanced magnetic resonance imaging; Sparse adaptive representation; Unsupervised tissue segmentation