Local Phase-Based Learning for Needle Detection and Localization in 3D Ultrasound

摘要

Described here is a novel method for automatic detection and enhancement of needles under 3D ultrasound guidance. We develop a detector consisting of a linear learning-based pixel classifier that utilizes Histogram of Oriented Gradients descriptors extracted from local phase projections. The detector automatically identifies slices of the volume that contain needle data, reducing the needle search space. Needle tip enhancement is performed on a projection of the extracted sub-volume, followed by automatic tip localization using spatially distributed image statistics within the trajectory constrained region. Evaluation of the proposed method on 40 volumes of ex vivo bovine tissue shows 88% detection precision, 98% recall rate, mean classification time per slice of 0.06 s and mean tip localization error of 0.44 ± 0.13 mm. The promising results indicate potential of the method for further evaluation on clinical pain management procedures.