title: Robust segmentation of mass-lesions in contrast-enhanced dynamic breast MR images.
abstract: OBJECTIVE
  To develop and evaluate a computerized segmentation method for breast MRI (BMRI) mass-lesions.
  
  
  METHODS
  A computerized segmentation algorithm was developed to segment mass-like-lesions on breast MRI. The segmentation algorithm involved: (i) interactive lesion selection, (ii) automatic intensity threshold estimation, (iii) connected component analysis, and (iv) a postprocessing procedure for hole-filling and leakage removal. Seven observers manually traced the borders of all slices of 30 mass-lesions using the same tools. To initiate the computerized segmentation, each user selected a seed-point for each lesion interactively using two methods: direct seed-point and robust region of interest (ROI) selections. The manual and computerized segmentations were compared pair-wise using the measured size and overlap to evaluate similarity, and the reproducibility of the computerized segmentation was compared with the interobserver variability of the manual delineations.
  
  
  RESULTS
  The observed inter- and intraobserver variations were similar (P > 0.05). Computerized segmentation using the robust ROI selection method was significantly (P < 0.001) more reproducible in measuring lesion size (stDev 1.8%) than either manual contouring (11.7%) or computerized segmentation using directly placed seed-point method (13.7%).
  
  
  CONCLUSIONS
  The computerized segmentation method using robust ROI selection is more reproducible than manual delineation in terms of measuring the size of a mass-lesion.
authors.affiliation: Clinical Site Research Program, Philips Research North America, Briarcliff Manor, New York 10510-2099, USA. lina.arbash-meinel@philips.com
authors.forename: Lina A
authors.initials: LA
authors.lastname: Meinel
authors.forename: Thomas
authors.initials: T
authors.lastname: Buelow
authors.forename: Dezheng
authors.initials: D
authors.lastname: Huo
authors.forename: Akiko
authors.initials: A
authors.lastname: Shimauchi
authors.forename: Ursula
authors.initials: U
authors.lastname: Kose
authors.forename: Johannes
authors.initials: J
authors.lastname: Buurman
authors.forename: Gillian
authors.initials: G
authors.lastname: Newstead
mesh_terms: D000465:Algorithms; D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000473:pathology*; D003287:Contrast Media; D005260:Female; D019786:Gadolinium DTPA; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D015203:Reproducibility of Results
chemical_list: D003287:Contrast Media; C064925:gadodiamide; D019786:Gadolinium DTPA