A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation. (1st July 2015)

• Record Type: Journal Article
• Authors: Militello, Carmelo; Vitabile, Salvatore; Rundo, Leonardo; Russo, Giorgio; Midiri, Massimo; Gilardi, Maria Carla
• Is Part Of: Computers in biology and medicine. Volume 62(2015)
• Page Start: 277
• Page End: 292
• Publication Date: 2015-07-01
• Subjects: Automatic segmentation -- Fuzzy C-Means clustering -- Adaptive thresholding -- MRgFUS treatment -- Uterine fibroids; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.04.030
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Purpose: 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. Method: 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. Graphical abstract: Highlights: The proposed method performs a fully automatic uterus and fibroids segmentation. We provide the boundary and volume of ablated fibroid regions and uterus. Our approach is based on FCM and iterative optimal threshold selection algorithms. This solution could optimize the current operative MRgFUS methodology. Our approach can improve the follow-up of patients undergone MRgFUS treatments.