title: A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation.
abstract: OBJECTIVE
  Magnetic Resonance guided Focused UltraSound (MRgFUS) represents a non-invasive surgical approach that uses thermal ablation to treat uterine fibroids. After the MRgFUS treatment, an operator must manually segment the treated fibroid areas to evaluate the NonPerfused Volume (NPV). This manual approach is operator-dependent, introducing issues of result reproducibility, which could lead to errors in the subsequent follow-up phase. Moreover, manual segmentation is time-consuming, and can have a negative impact on the optimization of both machine-time and operator-time.
  
  
  METHODS
  To address these issues, in this paper a novel fully automatic method based on the unsupervised Fuzzy C-Means clustering and iterative optimal threshold selection algorithms for uterus and fibroid segmentation is proposed. The developed method could be used to enhance the current manual methodology performed by healthcare operators for post-operative NPV evaluation in uterine fibroid MRgFUS treatments.
  
  
  RESULTS
  The proposed method was tested on 15 MR datasets of 15 different patients with uterine fibroids and evaluated using area-based and distance-based metrics. A comparison of extracted volume was also performed. Average values for fibroid (ROT) segmentation are SDI=88.67%, JI=80.70%, SE=89.79%, SP=88.73%, MAD=2.200 [pixels], MAXD=6.233 [pixels] and HD=2.988 [pixels]. Moreover, to make a quantitative evaluation of this method, our experimental results were compared with similar literature approaches.
  
  
  CONCLUSIONS
  The proposed method provides a practical approach for the automatic evaluation of the boundary and volume of ablated fibroid regions, without any external user input. The achieved segmentation results show the validity and the effectiveness of the proposed solution.
authors.affiliation: Istituto di Bioimmagini e Fisiologia Molecolare - Consiglio Nazionale delle Ricerche (IBFM CNR - LATO), Cefalù, PA, Italy. Electronic address: carmelo.militello@ibfm.cnr.it.
authors.forename: Carmelo
authors.initials: C
authors.lastname: Militello
authors.affiliation: Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi (DIBIMEF), Università degli Studi di Palermo, Palermo, Italy.
authors.forename: Salvatore
authors.initials: S
authors.lastname: Vitabile
authors.affiliation: Istituto di Bioimmagini e Fisiologia Molecolare - Consiglio Nazionale delle Ricerche (IBFM CNR - LATO), Cefalù, PA, Italy.
authors.forename: Leonardo
authors.initials: L
authors.lastname: Rundo
authors.affiliation: Istituto di Bioimmagini e Fisiologia Molecolare - Consiglio Nazionale delle Ricerche (IBFM CNR - LATO), Cefalù, PA, Italy.
authors.forename: Giorgio
authors.initials: G
authors.lastname: Russo
authors.affiliation: Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi (DIBIMEF), Università degli Studi di Palermo, Palermo, Italy.
authors.forename: Massimo
authors.initials: M
authors.lastname: Midiri
authors.affiliation: Istituto di Bioimmagini e Fisiologia Molecolare - Consiglio Nazionale delle Ricerche (IBFM CNR - LATO), Cefalù, PA, Italy.
authors.forename: Maria Carla
authors.initials: MC
authors.lastname: Gilardi
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007889:Leiomyoma / Q000000981:diagnostic imaging* / Q000628:therapy*; D008279:Magnetic Resonance Imaging; D011859:Radiography; D018084:Ultrasonography, Interventional
keywords: Adaptive thresholding; Automatic segmentation; Fuzzy C-Means clustering; MRgFUS treatment; Uterine fibroids