Militello Carmelo, Vitabile Salvatore, Rundo Leonardo, Russo Giorgio, Midiri Massimo, Gilardi Maria Carla
Istituto di Bioimmagini e Fisiologia Molecolare - Consiglio Nazionale delle Ricerche (IBFM CNR - LATO), Cefalù, PA, Italy.
Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi (DIBIMEF), Università degli Studi di Palermo, Palermo, Italy.
Comput Biol Med. 2015 Jul;62:277-92. doi: 10.1016/j.compbiomed.2015.04.030. Epub 2015 Apr 28.
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.
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.
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.
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.
磁共振引导聚焦超声（MRgFUS）是一种利用热消融治疗子宫肌瘤的非侵入性手术方法。在MRgFUS治疗后，操作人员必须手动分割治疗后的肌瘤区域，以评估无灌注体积（NPV）。这种手动方法依赖于操作人员，存在结果可重复性的问题，这可能导致后续随访阶段出现误差。此外，手动分割耗时，会对机器时间和操作人员时间的优化产生负面影响。
为了解决这些问题，本文提出了一种基于无监督模糊C均值聚类和迭代最优阈值选择算法的子宫和肌瘤分割全自动新方法。所开发的方法可用于改进医疗保健操作人员目前在子宫肌瘤MRgFUS治疗术后NPV评估中执行的手动方法。
该方法在15例不同子宫肌瘤患者的15个MR数据集中进行了测试，并使用基于面积和基于距离的指标进行评估。还对提取的体积进行了比较。肌瘤（ROT）分割的平均值为SDI = 88.67%，JI = 80.70%，SE = 89.79%，SP = 88.73%，MAD = 2.200[像素]，MAXD = 6.233[像素]，HD = 2.988[像素]。此外，为了对该方法进行定量评估，将我们的实验结果与类似的文献方法进行了比较。
所提出的方法提供了一种无需任何外部用户输入即可自动评估消融肌瘤区域边界和体积的实用方法。所取得的分割结果表明了所提解决方案的有效性和实用性。
