Random Forest-Based Bone Segmentation in Ultrasound

Abstract

Ultrasound (US) imaging is a safe alternative to radiography for guidance during minimally invasive orthopedic procedures. However, ultrasound is challenging to interpret because of the relatively low signal-to-noise ratio and its inherent speckle pattern that decreases image quality. Here we describe a method for automatic bone segmentation in 2-D ultrasound images using a patch-based random forest classifier and several ultrasound specific features, such as shadowing. We illustrate that existing shadow features are not robust to changes in US acquisition parameters, and propose a novel robust shadow feature. We evaluate the method on several US data sets and report that it favorably compares with existing techniques. We achieve a recall of 0.86 at a precision of 0.82 on a test set of 143 spinal US images.

Keywords: Intra-operative; Machine learning; Orthopedic procedure; Spine; Ultrasound; Ultrasound guidance; Vertebra.