title: Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans.
abstract: OBJECTIVE
  Toward an efficient clinical management of hepatocellular carcinoma (HCC), we propose a classification framework dedicated to tumor necrosis rate estimation from dynamic contrast-enhanced CT scans. Based on machine learning, it requires weak interaction efforts to segment healthy, active and necrotic liver tissues.
  
  
  METHODS
  Our contributions are two-fold. First, we apply random forest (RF) on supervoxels using multi-phase supervoxel-based features that discriminate tissues based on their dynamic in response to contrast agent injection. Second, we extend this technique in a hierarchical multi-scale fashion to deal with multiple spatial extents and appearance heterogeneity. It translates in an adaptive data sampling scheme combining RF and hierarchical multi-scale tree resulting from recursive supervoxel decomposition. By concatenating multi-phase features across the hierarchical multi-scale tree to describe leaf supervoxels, we enable RF to automatically infer the most informative scales without defining any explicit rules on how to combine them.
  
  
  RESULTS
  Assessment on clinical data confirms the benefits of multi-phase information embedded in a multi-scale supervoxel representation for HCC tumor segmentation.
  
  
  CONCLUSIONS
  Dedicated but not limited only to HCC management, both contributions reach further steps toward more accurate multi-label tissue classification.
authors: ICube UMR 7357, University of Strasbourg, CNRS, FMTS, 300 bd Sébastien Brant, 67412, Illkirch, France. conze@unistra.fr.
authors: Pierre-Henri
authors: PH
authors: Conze
authors: ICube UMR 7357, University of Strasbourg, CNRS, FMTS, 300 bd Sébastien Brant, 67412, Illkirch, France.
authors: Vincent
authors: V
authors: Noblet
authors: Institut Mines-Télécom, Télécom Bretagne, INSERM, LATIM UMR 1101, Technopôle Brest-Iroise, 29238, Brest, France.
authors: François
authors: F
authors: Rousseau
authors: ICube UMR 7357, University of Strasbourg, CNRS, FMTS, 300 bd Sébastien Brant, 67412, Illkirch, France.
authors: Fabrice
authors: F
authors: Heitz
authors: Department of Hepato-Biliary and Pancreatic Surgery, Nouvel Hôpital Civil, Institut Hospitalo-Universitaire de Strasbourg, 1 place de l'Hôpital, 67000, Strasbourg, France.
authors: Vito
authors: V
authors: de Blasi
authors: Department of Hepato-Biliary and Pancreatic Surgery, Nouvel Hôpital Civil, Institut Hospitalo-Universitaire de Strasbourg, 1 place de l'Hôpital, 67000, Strasbourg, France.
authors: Riccardo
authors: R
authors: Memeo
authors: Department of Hepato-Biliary and Pancreatic Surgery, Nouvel Hôpital Civil, Institut Hospitalo-Universitaire de Strasbourg, 1 place de l'Hôpital, 67000, Strasbourg, France.
authors: Patrick
authors: P
authors: Pessaux
mesh_terms: D000465:Algorithms; D006528:Carcinoma, Hepatocellular / Q000000981:diagnostic imaging*; D003287:Contrast Media; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods
chemical_list: D003287:Contrast Media
keywords: Dynamic features; Hierarchical multi-scale tree; Liver tumor segmentation; Random forest; Spatial adaptivity; Supervoxels