Purpose

Reduction and osteosynthesis of ankle fractures is a challenging surgical procedure when it comes to the verification of the reduction result. Evaluation is conducted using intra-operative imaging of the injured ankle and depends on the expertise of the surgeon. Studies suggest that intra-individual variance of the ankle bone shape and pose is considerably lower than the inter-individual variance. It stands to reason that the information gain from the healthy contralateral side can help to improve the evaluation.

Method

In this paper, an assistance system is proposed that provides a side-to-side view of the two ankle joints for visual comparison and instant evaluation using only one 3D C-arm image. Two convolutional neural networks (CNN) are employed to extract the relevant image regions and pose information of each ankle so that they can be aligned with each other. A first U-Net uses a sliding window to predict the location of each ankle. The standard plane estimation is formulated as segmentation problem so that a second U-Net predicts the three viewing planes for alignment.

Results

Experiments were conducted to assess the accuracy of the individual steps on 218 unilateral ankle datasets as well as the overall performance on 7 bilateral ankle datasets. The experiments on unilateral ankles yield a median position-to-plane error of \(0.73\pm 1.36\) mm and a median angular error between 2.98\(^\circ \) and 3.71\(^\circ \) for the plane normals.

Conclusion

Standard plane estimation via segmentation outperforms direct pose regression. Furthermore, the complete pipeline was evaluated including ankle detection and subsequent plane estimation on bilateral datasets. The proposed pipeline enables a direct contralateral side comparison without additional radiation. This has the potential to ease and improve the intra-operative evaluation for the surgeons in the future and reduce the need for revision surgery.

中文翻译：

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使用平板技术的踝关节的计算机辅助对侧侧面比较

目的

在验证复位结果时，踝部骨折的复位和骨合成是一项具有挑战性的外科手术。评估是通过术中对受伤的脚踝进行的成像来进行的，并且取决于外科医生的专业知识。研究表明，踝骨形状和姿势的个体内差异明显低于个体间差异。从健康的对侧获得的信息可以帮助改善评估，这是有理由的。

方法

在本文中，提出了一种辅助系统，该系统仅使用一个3D C臂图像即可提供两个脚踝关节的侧视图，以进行视觉比较和即时评估。使用两个卷积神经网络（CNN）提取相关图像区域并姿势每个脚踝的信息，以便它们可以彼此对齐。第一U-Net使用滑动窗口来预测每个脚踝的位置。将标准平面估计公式化为分割问题，以便第二个U-Net预测三个用于对齐的观察平面。

结果

进行实验以评估218个单侧脚踝数据集上各个步骤的准确性以及7个双侧脚踝数据集上的整体性能。上单侧脚踝实验得到的中值位置到平面误差\（0.73 \下午1.36 \）毫米和中值角度误差2.98之间\（^ \ CIRC \）和3.71 \（^ \ CIRC \）为平面常态。

结论

通过分段进行的标准平面估计优于直接姿态回归。此外，评估了完整的管道，包括在双侧数据集上进行的脚踝检测和随后的平面估计。拟议中的管线可以直接进行对侧比较，而无需额外辐射。将来有可能缓解和改善外科医生的术中评估，并减少翻修手术的需要。