Strain-Initialized Robust Bone Surface Detection in 3-D Ultrasound

Abstract

Three-dimensional ultrasound has been increasingly considered as a safe radiation-free alternative to radiation-based fluoroscopic imaging for surgical guidance during computer-assisted orthopedic interventions, but because ultrasound images contain significant artifacts, it is challenging to automatically extract bone surfaces from these images. We propose an effective way to extract 3-D bone surfaces using a surface growing approach that is seeded from 2-D bone contours. The initial 2-D bone contours are estimated from a combination of ultrasound strain images and envelope power images. Novel features of the proposed method include: (i) improvement of a previously reported 2-D strain imaging-based bone segmentation method by incorporation of a depth-dependent cumulative power of the envelope into the elastographic data; (ii) incorporation of an echo decorrelation measure-based weight to fuse the strain and envelope maps; (iii) use of local statistics of the bone surface candidate points to detect the presence of any bone discontinuity; and (iv) an extension of our 2-D bone contour into a 3-D bone surface by use of an effective surface growing approach. Our new method produced average improvements in the mean absolute error of 18% and 23%, respectively, on 2-D and 3-D experimental phantom data, compared with those of two state-of-the-art bone segmentation methods. Validation on 2-D and 3-D clinical in vivo data also reveals, respectively, an average improvement in the mean absolute fitting error of 55% and an 18-fold improvement in the computation time.

Keywords: Bone; Gaussian mixture Regression; Segmentation; Strain imaging; Surface growing; Ultrasound.