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Automatic segmentation for detecting uterine fibroid regions treated with MR‐guided high intensity focused ultrasound (MR‐HIFU)
Author(s) -
Antila Kari,
Nieminen Heikki J.,
Sequeiros Roberto Blanco,
Ehnholm Gösta
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4881319
Subject(s) - segmentation , computer science , uterine fibroids , high intensity focused ultrasound , radiation treatment planning , artificial intelligence , image segmentation , volume (thermodynamics) , ultrasound , radiology , similarity (geometry) , 3d ultrasound , medicine , surgery , image (mathematics) , radiation therapy , physics , quantum mechanics
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 or a 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.