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Computer-Assisted Approaches for Uterine Fibroid Segmentation in MRgFUS Treatments: Quantitative Evaluation and Clinical Feasibility Analysis

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Part of the Smart Innovation, Systems and Technologies book series (SIST,volume 103)

Abstract

Nowadays, uterine fibroids can be treated using Magnetic Resonance guided Focused Ultrasound Surgery (MRgFUS), which is a non-invasive therapy exploiting thermal ablation. In order to measure the Non-Perfused Volume (NPV) for treatment response assessment, the ablated fibroid areas (i.e., Region of Treatment, ROT) are manually contoured by a radiologist. The current operator-dependent methodology could affect the subsequent follow-up phases, due to the lack of result repeatability. In addition, this fully manual procedure is time-consuming, considerably increasing execution times. These critical issues can be addressed only by means of accurate and efficient automated Pattern Recognition approaches. In this contribution, we evaluate two computer-assisted segmentation methods, which we have already developed and validated, for uterine fibroid segmentation in MRgFUS treatments. A quantitative comparison on segmentation accuracy, in terms of area-based and distance-based metrics, was performed. The clinical feasibility of these approaches was assessed from physicians’ perspective, by proposing an integrated solution.

Keywords

  • Computer-assisted medical image segmentation
  • Pattern Recognition
  • Magnetic Resonance guided Focused Ultrasound Surgery
  • Uterine fibroids
  • Non-Perfused Volume assessment
  • Clinical feasibility

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  • DOI: 10.1007/978-3-319-95095-2_22
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Correspondence to Carmelo Militello .

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Rundo, L. et al. (2019). Computer-Assisted Approaches for Uterine Fibroid Segmentation in MRgFUS Treatments: Quantitative Evaluation and Clinical Feasibility Analysis. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Quantifying and Processing Biomedical and Behavioral Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 103. Springer, Cham. https://doi.org/10.1007/978-3-319-95095-2_22

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