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Adaptive localised region and edge-based active contour model using shape constraint and sub-global information for uterine fibroid segmentation in ultrasound-guided HIFU therapy

Adaptive localised region and edge-based active contour model using shape constraint and sub-global information for uterine fibroid segmentation in ultrasound-guided HIFU therapy

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Uterine fibroids segmentation in ultrasound images is of great importance in the definition of intra-operative planning of ultrasound-guided high-intensity focused ultrasound (HIFU) therapy. However, it is challenging to obtain accurate, robust and efficient uterine fibroid segmentation due to low quality of ultrasound images. In this study, the authors propose a novel adaptive localised region and edge-based active contour model using shape constraint and sub-global information to accurately and efficiently segment the uterine fibroids in ultrasound images with robustness against initial contour. The authors first define adaptive local radius for the localised region-based model and combine it with the edge-based model to accurately and efficiently capture image's heterogeneous features and edge features. Then, they incorporate a shape constraint to reduce boundary leakage or excessive contraction to obtain more accurate segmentation. To overcome the initialisation sensitivity, they introduce the sub-global information to prevent the curve from trapping into the local minima and obtain robust results. Furthermore, the authors optimise computation by adaptively sharing local region and employing the multi-scale segmentation method to achieve efficient segmentation. The proposed method is validated by uterine fibroid ultrasound images in HIFU therapy and the results demonstrate that it can achieve accurate, robust and efficient segmentation.

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