title: Automatic segmentation for detecting uterine fibroid regions treated with MR-guided high intensity focused ultrasound (MR-HIFU).
abstract: OBJECTIVE
  Up to 25% of women suffer from uterine fibroids (UF) that cause infertility, pain, and discomfort. MR-guided high intensity focused ultrasound (MR-HIFU) is an emerging technique for noninvasive, computer-guided thermal ablation of UFs. The volume of induced necrosis is a predictor of the success of the treatment. However, accurate volume assessment by hand can be time consuming, and quick tools produce biased results. Therefore, fast and reliable tools are required in order to estimate the technical treatment outcome during the therapy event so as to predict symptom relief.
  
  
  METHODS
  A novel technique has been developed for the segmentation and volume assessment of the treated region. Conventional algorithms typically require user interaction ora priori knowledge of the target. The developed algorithm exploits the treatment plan, the coordinates of the intended ablation, for fully automatic segmentation with no user input.
  
  
  RESULTS
  A good similarity to an expert-segmented manual reference was achieved (Dice similarity coefficient = 0.880 ± 0.074). The average automatic segmentation time was 1.6 ± 0.7 min per patient against an order of tens of minutes when done manually.
  
  
  CONCLUSIONS
  The results suggest that the segmentation algorithm developed, requiring no user-input, provides a feasible and practical approach for the automatic evaluation of the boundary and volume of the HIFU-treated region.
authors.affiliation: VTT Technical Research Centre of Finland, Tampere, FI-33200 Tampere, Finland.
authors.forename: Kari
authors.initials: K
authors.lastname: Antila
authors.affiliation: MR-Therapy, Philips Healthcare, FI-01511 Vantaa, Finland and Department of Physics, University of Helsinki, FI-00014, Helsinki, Finland.
authors.forename: Heikki J
authors.initials: HJ
authors.lastname: Nieminen
authors.affiliation: South Western Finland Imaging Centre, Turku University Hospital and Turku University, FI-20521, Turku, Finland.
authors.forename: Roberto Blanco
authors.initials: RB
authors.lastname: Sequeiros
authors.affiliation: MR-Therapy, Philips Healthcare, FI-01511 Vantaa, Finland.
authors.forename: Gösta
authors.initials: G
authors.lastname: Ehnholm
mesh_terms: D000465:Algorithms; D005240:Feasibility Studies; D005260:Female; D057086:High-Intensity Focused Ultrasound Ablation / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007889:Leiomyoma / Q000473:pathology* / Q000628:therapy*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors