Brehler Michael, Görres Joseph, Vetter Sven Y, Franke Jochen, Grützner Paul A, Meinzer Hans-Peter, Wolf Ivo
Division of Medical and Biological Informatics (E130), German Cancer Research Center, Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
BG Trauma Center, Ludwig-Guttmann-Strae 13, 67071, Ludwigshafen, Germany.
Int J Comput Assist Radiol Surg. 2016 Apr;11(4):603-12. doi: 10.1007/s11548-015-1304-0. Epub 2015 Oct 8.
The assessment of intra-operatively acquired volumetric data is a difficult and often time-consuming task, which demands a new set of skills from the surgeons. In the case of orthopedic surgeries such as the treatment of calcaneal fractures, the correctness of the reduction of the bone fragments can be verified with the help of C-arm CT volumetric images. For an accurate intra-operative assessment of the displaced fragments, an automatic segmentation of the articular surfaces and color-coded visualization was developed.
Our automatic approach consists of three major steps: first, using adjusted standard planes intersecting the articular region, the joint space is localized with an intensity profile-based method. In a second step, the localized joint space is segmented on the Laplacian of Gaussian filtered volumetric image by a modified binary flood fill algorithm. Finally, a 3D surface model of the segmented joint space is analyzed and visualized with focus on critical displacements of the surface.
A specifically designed human cadaver study consisting of ten lower legs of ten different donors was conducted to acquire 48 realistic C-arm CT images of misaligned bone fragments (steps of varying sizes) in the posterior talar articular surface of the calcaneus. The proposed algorithmic pipeline was verified by the acquired image data and showed very good results with no false positives and an overall correct displacement assessment of 93.8%.
The proposed algorithmic pipeline can be easily integrated into the clinical workflow and qualifies for intra-operative usage. It showed very good results on the reference data set of the cadaver study. With the help of such an assistance system, the time-consuming process of 2D view adjustment and visual assessment of the gray value images can be greatly simplified.
术中获取的容积数据评估是一项困难且耗时的任务,需要外科医生掌握一套新技能。在诸如跟骨骨折治疗等骨科手术中,借助C型臂CT容积图像可验证骨碎片复位的正确性。为了在术中准确评估移位的碎片,开发了一种关节面自动分割和颜色编码可视化方法。
我们的自动方法包括三个主要步骤:首先,使用与关节区域相交的调整标准平面,通过基于强度轮廓的方法定位关节间隙。第二步,在高斯滤波后的容积图像的拉普拉斯算子上,通过改进的二进制泛洪填充算法对定位的关节间隙进行分割。最后,对分割后的关节间隙的三维表面模型进行分析,并着重关注表面的关键位移进行可视化。
进行了一项专门设计的人体尸体研究,该研究由来自十位不同捐赠者的十条小腿组成,以获取48张跟骨距骨后关节面错位骨碎片(不同大小的步长)的逼真C型臂CT图像。所提出的算法流程通过获取的图像数据得到验证,结果非常好,无假阳性,整体位移评估正确率为93.8%。
所提出的算法流程可轻松集成到临床工作流程中,适用于术中使用。在尸体研究的参考数据集上显示出非常好的结果。借助这样的辅助系统,二维视图调整和灰度值图像视觉评估这一耗时过程可大大简化。