Automatic segmentation for detecting uterine fibroid regions treated with MR-guided high intensity focused ultrasound (MR-HIFU)

Kari Antila, H.J. Nieminen, R.B. Sequeiros, G. Ehnholm

    Research output: Contribution to journalArticleScientificpeer-review

    11 Citations (Scopus)


    Purpose: 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
    Original languageEnglish
    Article number073502
    JournalMedical Physics
    Issue number7
    Publication statusPublished - 2014
    MoE publication typeA1 Journal article-refereed


    • high-intensity focused ultrasound
    • image segmentation
    • treatment volume
    • uterine fibroid
    • pipelines
    • medical image segmentation
    • medical magnetic resonance imaging
    • tissue ablation
    • tissues


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