Hussain Mohammad Arafat, Hodgson Antony J, Abugharbieh Rafeef
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
Department of Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
Ultrasound Med Biol. 2017 Mar;43(3):648-661. doi: 10.1016/j.ultrasmedbio.2016.11.003. Epub 2016 Dec 23.
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.
在计算机辅助骨科手术中,三维超声越来越被视为一种安全且无辐射的替代基于辐射的荧光透视成像的手术引导方法。然而,由于超声图像包含大量伪像,从这些图像中自动提取骨表面具有挑战性。我们提出了一种有效的方法,使用从二维骨轮廓开始的表面生长方法来提取三维骨表面。初始的二维骨轮廓是根据超声应变图像和包络功率图像的组合估计出来的。该方法的新颖之处包括:(i)通过将包络的深度相关累积功率纳入弹性成像数据,改进了先前报道的基于二维应变成像的骨分割方法;(ii)纳入基于回波去相关测量的权重来融合应变图和包络图;(iii)利用骨表面候选点的局部统计来检测任何骨不连续的存在;(iv)通过使用有效的表面生长方法将我们的二维骨轮廓扩展为三维骨表面。与两种最先进的骨分割方法相比,我们的新方法在二维和三维实验模型数据上分别使平均绝对误差平均提高了18%和23%。在二维和三维临床体内数据上的验证还分别显示,平均绝对拟合误差平均提高了55%,计算时间提高了18倍。