title: Automatic segmentation of whole breast using atlas approach and deformable image registration.
abstract: OBJECTIVE
  To compare interobserver variations in delineating the whole breast for treatment planning using two contouring methods.
  
  
  METHODS
  Autosegmented contours were generated by a deformable image registration-based breast segmentation method (DEF-SEG) by mapping the whole breast clinical target volume (CTVwb) from a template case to a new patient case. Eight breast radiation oncologists modified the autosegmented contours as necessary to achieve a clinically appropriate CTVwb and then recontoured the same case from scratch for comparison. The times to complete each approach, as well as the interobserver variations, were analyzed. The template case was also mapped to 10 breast cancer patients with a body mass index of 19.1-35.9 kg/m(2). The three-dimensional surface-to-surface distances and volume overlapping analyses were computed to quantify contour variations.
  
  
  RESULTS
  The median time to edit the DEF-SEG-generated CTVwb was 12.9 min (range, 3.4-35.9) compared with 18.6 min (range, 8.9-45.2) to contour the CTVwb from scratch (30% faster, p = 0.028). The mean surface-to-surface distance was noticeably reduced from 1.6 mm among the contours generated from scratch to 1.0 mm using the DEF-SEG method (p = 0.047). The deformed contours in 10 patients achieved 94% volume overlap before correction and required editing of 5% (range, 1-10%) of the contoured volume.
  
  
  CONCLUSIONS
  Significant interobserver variations suggested a lack of consensus regarding the CTVwb, even among breast cancer specialists. Using the DEF-SEG method produced more consistent results and required less time. The DEF-SEG method can be successfully applied to patients with different body mass indexes.
authors.affiliation: Department of Radiation Oncology, University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA.
authors.forename: Valerie K
authors.initials: VK
authors.lastname: Reed
authors.forename: Wendy A
authors.initials: WA
authors.lastname: Woodward
authors.forename: Lifei
authors.initials: L
authors.lastname: Zhang
authors.forename: Eric A
authors.initials: EA
authors.lastname: Strom
authors.forename: George H
authors.initials: GH
authors.lastname: Perkins
authors.forename: Welela
authors.initials: W
authors.lastname: Tereffe
authors.forename: Julia L
authors.initials: JL
authors.lastname: Oh
authors.forename: T Kuan
authors.initials: TK
authors.lastname: Yu
authors.forename: Isabelle
authors.initials: I
authors.lastname: Bedrosian
authors.forename: Gary J
authors.initials: GJ
authors.lastname: Whitman
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Buchholz
authors.forename: Lei
authors.initials: L
authors.lastname: Dong
mesh_terms: D000465:Algorithms; D015992:Body Mass Index; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy; D004526:Efficiency; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008327:Mammography / Q000379:methods*; D015588:Observer Variation; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D013997:Time Factors; D014057:Tomography, X-Ray Computed / Q000379:methods