论文标题
在B模式图像中描述骨表面，受到超声传播物理的约束
Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation
论文作者
论文摘要
超声中的骨表面描述引起了人们的关注，这是由于其在骨科中的诊断，手术计划和术后随访的潜力，以及在手术导航中使用骨骼作为解剖学地标的潜力。我们在这里提出了一种将超声传播物理学编码为骨表面描述目的的因子图制定的方法。在此图结构中，单个节点电势编码是通过图像描述符学习的软组织或声阴影（骨表面背后）区域的局部可能性。鉴于其较大的声音阻抗差异，配对边缘电势编码骨表面的超声传播约束。我们评估了所提出的方法与从前臂背侧和沃尔的视图收集的体内超声图像上的四种方法相比，我们评估了所提出的方法。所提出的方法分别达到平均根平方误差和对称的Hausdorff距离为0.28mm和1.78mm。它检测到99.9％的带注释的骨表面，平均扫描线误差（注释距离）为0.39mm。
Bone surface delineation in ultrasound is of interest due to its potential in diagnosis, surgical planning, and post-operative follow-up in orthopedics, as well as the potential of using bones as anatomical landmarks in surgical navigation. We herein propose a method to encode the physics of ultrasound propagation into a factor graph formulation for the purpose of bone surface delineation. In this graph structure, unary node potentials encode the local likelihood for being a soft tissue or acoustic-shadow (behind bone surface) region, both learned through image descriptors. Pair-wise edge potentials encode ultrasound propagation constraints of bone surfaces given their large acoustic-impedance difference. We evaluate the proposed method in comparison with four earlier approaches, on in-vivo ultrasound images collected from dorsal and volar views of the forearm. The proposed method achieves an average root-mean-square error and symmetric Hausdorff distance of 0.28mm and 1.78mm, respectively. It detects 99.9% of the annotated bone surfaces with a mean scanline error (distance to annotations) of 0.39mm.
