title: Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast.
abstract: OBJECTIVE
  Contrast-enhanced T1-weighted imaging is usually included in MRI procedures for automatic tumor segmentation. Use of an MR contrast agent may not be appropriate for some applications, however. We assessed the feasability of automatic tumor segmentation by multiparametric cluster analysis that uses intrinsic MRI contrast only.
  
  
  METHODS
  Multiparametric MRI consisting of quantitative T1, T2, and apparent diffusion coefficient (ADC) mapping was performed in mice bearing subcutaneous tumors (n = 21). k-means and fuzzy c-means clustering with all possible combinations of MRI parameters, i.e. feature vectors, and 2-7 clusters were performed on the multiparametric data. Clusters associated with tumor tissue were selected on the basis of the relative signal intensity of tumor tissue in T2-weighted images. The optimum segmentation method was determined by quantitative comparison of automatic segmentation with manual segmentation performed by three observers. In addition, the automatically segmented tumor volumes from seven separate tumor data sets were quantitatively compared with histology-derived tumor volumes.
  
  
  RESULTS
  The highest similarity index between manual and automatic segmentation (SI manual,automatic = 0.82 ± 0.06) was observed for k-means clustering with feature vector {T2, ADC} and four clusters. A strong linear correlation between automatically and manually segmented tumor volumes (R (2) = 0.99) was observed for this segmentation method. Automatically segmented tumor volumes also correlated strongly with histology-derived tumor volumes (R (2) = 0.96).
  
  
  CONCLUSIONS
  Automatic segmentation of mouse subcutaneous tumors can be achieved on the basis of endogenous MR contrast only.
authors.affiliation: Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands, s.j.c.g.hectors@tue.nl.
authors.forename: Stefanie J C G
authors.initials: SJ
authors.lastname: Hectors
authors.forename: Igor
authors.initials: I
authors.lastname: Jacobs
authors.forename: Gustav J
authors.initials: GJ
authors.lastname: Strijkers
authors.forename: Klaas
authors.initials: K
authors.lastname: Nicolay
mesh_terms: D000818:Animals; D005240:Feasibility Studies; D006614:Hindlimb; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D051379:Mice; D008807:Mice, Inbred BALB C; D010363:Pattern Recognition, Automated / Q000379:methods*; D012983:Soft Tissue Neoplasms / Q000473:pathology*; D014407:Tumor Cells, Cultured