Purpose

Real-time, two (2D) and three-dimensional (3D) ultrasound (US) has been investigated as a potential alternative to fluoroscopy imaging in various surgical and non-surgical orthopedic procedures. However, low signal to noise ratio, imaging artifacts and bone surfaces appearing several millimeters (mm) in thickness have hindered the wide spread adaptation of this safe imaging modality. Limited field of view and manual data collection cause additional problems during US-based orthopedic procedures. In order to overcome these limitations various bone segmentation and registration methods have been developed. Acoustic bone shadow is an important image artifact used to identify the presence of bone boundaries in the collected US data. Information about bone shadow region can be used (1) to guide the orthopedic surgeon or clinician to a standardized diagnostic viewing plane with minimal artifacts, (2) as a prior feature to improve bone segmentation and registration.

Method

In this work, we propose a computational method, based on a novel generative adversarial network (GAN) architecture, to segment bone shadow images from in vivo US scans in real-time. We also show how these segmented shadow images can be incorporated, as a proxy, to a multi-feature guided convolutional neural network (CNN) architecture for real-time and accurate bone surface segmentation. Quantitative and qualitative evaluation studies are performed on 1235 scans collected from 27 subjects using two different US machines. Finally, we provide qualitative and quantitative comparison results against state-of-the-art GANs.

Results

We have obtained mean dice coefficient (± standard deviation) of \(93\%\) (\(\pm \,0.02 \)) for bone shadow segmentation, showing that the method is in close range with manual expert annotation. Statistical significant improvements against state-of-the-art GAN methods (paired t-test \(p<0.05\)) is also obtained. Using the segmented bone shadow features average bone localization accuracy of 0.11 mm (\(\pm \,0.16 \)) was achieved.

Conclusions

Reported accurate and robust results make the proposed method promising for various orthopedic procedures. Although we did not investigate in this work, the segmented bone shadow images could also be used as an additional feature to improve accuracy of US-based registration methods. Further extensive validations are required in order to fully understand the clinical utility of the proposed method.

中文翻译：

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使用 GAN 从用于骨科手术的超声数据中分割出骨影。

目的

实时、二维 (2D) 和三维 (3D) 超声 (US) 已被研究作为各种外科和非外科骨科手术中透视成像的潜在替代方案。然而，低信噪比、成像伪影和几毫米 (mm) 厚的骨表面阻碍了这种安全成像方式的广泛应用。在美国的骨科手术过程中，有限的视野和手动数据收集会导致其他问题。为了克服这些限制，已经开发了各种骨骼分割和配准方法。声学骨阴影是一种重要的图像伪影，用于在收集的美国数据中识别骨骼边界的存在。

方法

在这项工作中，我们提出了一种基于新型生成对抗网络 (GAN) 架构的计算方法，以实时分割来自体内 US 扫描的骨阴影图像。我们还展示了如何将这些分割的阴影图像作为代理结合到多特征引导卷积神经网络 (CNN) 架构中，以进行实时和准确的骨表面分割。使用两台不同的美国机器对从 27 位受试者收集的 1235 次扫描进行了定量和定性评估研究。最后，我们提供了与最先进的 GAN 的定性和定量比较结果。

结果

我们已经获得了\(93\%\) ( \(\pm \,0.02 \) ) 的平均骰子系数（±标准差）用于骨阴影分割，表明该方法与手动专家注释接近。还获得了对最先进的 GAN 方法（配对t -test \(p<0.05\)）的统计显着改进。使用分段骨阴影特征，平均骨定位精度达到 0.11 mm ( \(\pm \,0.16 \) )。

结论

报告的准确和稳健的结果使所提出的方法有望用于各种骨科手术。虽然我们没有在这项工作中进行调查，但分割的骨阴影图像也可以用作附加特征，以提高基于 US 的配准方法的准确性。需要进一步广泛的验证才能充分了解所提出方法的临床效用。