Computer Assisted Research and Development Group, Balgrist University Hospital, University of Zurich, Forchstrasse 340, CH-8008 Zurich, Switzerland; Computer Vision Laboratory, ETH Zurich, Sternwartstrasse 7, CH-8092 Zürich, Switzerland.
Computer Vision Laboratory, ETH Zurich, Sternwartstrasse 7, CH-8092 Zürich, Switzerland.
Med Image Anal. 2018 Jan;43:142-156. doi: 10.1016/j.media.2017.10.006. Epub 2017 Oct 27.
The optimal surgical treatment of complex fractures of the proximal humerus is controversial. It is proven that best results are obtained if an anatomical reduction of the fragments is achieved and, therefore, computer-assisted methods have been proposed for the reconstruction of the fractures. However, complex fractures of the proximal humerus are commonly accompanied with a relevant displacement of the fragments and, therefore, algorithms relying on the initial position of the fragments might fail. The state-of-the-art algorithm for complex fractures of the proximal humerus requires the acquisition of a CT scan of the (healthy) contralateral anatomy as a reconstruction template to address the displacement of the fragments. Pose-invariant fracture line based reconstruction algorithms have been applied successful for reassembling broken vessels in archaeology. Nevertheless, the extraction of the fracture lines and the necessary computation of their curvature are susceptible to noise and make the application of previous approaches difficult or even impossible for bone fractures close to the joints, where the cortical layer is thin. We present a novel scale-space representation of the curvature, permitting to calculate the correct alignment between bone fragments solely based on corresponding regions of the fracture lines. The fractures of the proximal humerus are automatically reconstructed based on iterative pairwise reduction of the fragments. The validation of the presented method was performed on twelve clinical cases, surgically treated after complex proximal humeral fracture, and by cadaver experiments. The accuracy of our approach was compared to the state-of-the-art algorithm for complex fractures of the proximal humerus. All reconstructions of the clinical cases resulted in an accurate approximation of the pre-traumatic anatomy. The accuracy of the reconstructed cadaver cases outperformed the current state-of-the-art algorithm.
肱骨近端复杂骨折的最佳手术治疗仍存在争议。已证实，如果实现了骨折块的解剖复位，则可获得最佳效果，因此已经提出了计算机辅助方法来重建骨折。但是，肱骨近端复杂骨折通常伴随着骨折块的明显移位，因此，依赖骨折块初始位置的算法可能会失败。目前用于肱骨近端复杂骨折的算法需要获取（健康）对侧解剖结构的 CT 扫描作为重建模板，以解决骨折块的移位问题。基于不变位骨折线的重建算法已成功应用于考古中破碎血管的重组。然而，骨折线的提取和曲率的必要计算容易受到噪声的影响，使得以前的方法难以甚至不可能应用于接近关节的骨骨折，因为关节处的皮质层较薄。我们提出了一种新的曲率尺度空间表示方法，仅基于骨折线的对应区域即可计算骨碎片之间的正确对齐。肱骨近端骨折是基于骨折块的迭代对合来自动重建的。该方法的验证是在 12 例经过复杂肱骨近端骨折手术治疗的临床病例和尸体实验中进行的。将我们的方法的准确性与用于肱骨近端复杂骨折的最新算法进行了比较。所有临床病例的重建结果均实现了对创伤前解剖结构的准确逼近。重建尸体病例的准确性优于当前的最新算法。
