title: Semiautomatic parametric model-based 3D lesion segmentation for evaluation of MR-guided radiofrequency ablation therapy.
abstract: OBJECTIVE
  Interventional magnetic resonance imaging (iMRI) allows real-time guidance and optimization of radiofrequency ablation of pathologic tissue. For many tissues, resulting lesions have a characteristic two-boundary appearance featuring an inner region and an outer hyper-intense margin in both T2 and contrast-enhanced (CE) T1-weighted MR images. We created a geometric model-based semiautomatic method to aid in real-time lesion segmentation, cross-sectional/three-dimensional visualization, and intra/posttreatment evaluation.
  
  
  METHODS
  Our method relies on a 12-parameter, 3-dimensional, globally deformable model with quadric surfaces that describe both lesion boundaries. We present an energy minimization approach to quickly and semiautomatically fit the model to a gray-scale MR image volume. We applied the method to in vivo lesions (n = 10) in a rabbit thigh model, using T2 and CE T1-weighted MR images, and compared the results with manually segmented boundaries.
  
  
  RESULTS
  For all lesions, the median error was < or =1.21 mm for the inner region and < or =1.00 mm for the outer hyper-intense region, values that favorably compare to a voxel width of 0.7 mm and distances between the borders manually segmented by the two operators.
  
  
  CONCLUSIONS
  Our method provides a precise, semiautomatic approximation of lesion shape for ellipsoidal lesions. Further, the method has clinical applications in lesion visualization, volume estimation, and treatment evaluation.
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
authors.forename: Roee S
authors.initials: RS
authors.lastname: Lazebnik
authors.forename: Brent D
authors.initials: BD
authors.lastname: Weinberg
authors.forename: Michael S
authors.initials: MS
authors.lastname: Breen
authors.forename: Jonathan S
authors.initials: JS
authors.lastname: Lewin
authors.forename: David L
authors.initials: DL
authors.lastname: Wilson
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D017115:Catheter Ablation / Q000379:methods*; D003199:Computer Systems; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D018482:Muscle, Skeletal / Q000473:pathology* / Q000601:surgery*; D010363:Pattern Recognition, Automated / Q000379:methods; D011379:Prognosis; D011817:Rabbits; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*; D013848:Thigh / Q000473:pathology / Q000601:surgery