Tumor boundary detection in ultrasound imagery using multi-scale generalized gradient vector flow.

Purpose: As a key technology in high-intensity focused ultrasound (HIFU) ablation systems, a precise ultrasound image segmentation method for tumor boundary detection is helpful for ablation of tumors and avoiding tumor recurrence. This study explores a new deformable snake model called multi-scale generalized gradient vector flow (MS-GGVF) to segment ultrasound images in HIFU ablation.

Methods: The main idea of the technique is dealing with two issues including spurious boundary attenuation and setting the standard deviation of the Gaussian filter. We assign the standard deviation as scales to build the MS-GGVF model and create a signed distance map to use its gradient direction information and magnitude information to refine the multi-scale edge map by attenuating spurious boundaries and highlighting the real boundary. In addition, a fast generalized gradient vector flow computation algorithm based on an augmented Lagrangian method is introduced to calculate the external force vector field to improve the computation efficiency of our model.

Results: The experimental segmentations were similar to the ground truths delineated by two medical physicians with high area overlap measure and low mean contour distance.

Conclusion: The experimental results demonstrate that the proposed algorithm is robust, reliable, and precise for tumor boundary detection in HIFU ablation systems.