Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA.
Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA.
Int J Comput Assist Radiol Surg. 2019 May;14(5):775-783. doi: 10.1007/s11548-019-01934-0. Epub 2019 Mar 13.
Ultrasound (US) provides real-time, two-/three-dimensional safe imaging. Due to these capabilities, it is considered a safe alternative to intra-operative fluoroscopy in various computer-assisted orthopedic surgery (CAOS) procedures. However, interpretation of the collected bone US data is difficult due to high levels of noise, various imaging artifacts, and bone surfaces response appearing several millimeters (mm) in thickness. For US-guided CAOS procedures, it is an essential objective to have a segmentation mechanism, that is both robust and computationally inexpensive.
In this paper, we present our development of a convolutional neural network-based technique for segmentation of bone surfaces from in vivo US scans. The novelty of our proposed design is that it utilizes fusion of feature maps and employs multi-modal images to abate sensitivity to variations caused by imaging artifacts and low intensity bone boundaries. B-mode US images, and their corresponding local phase filtered images are used as multi-modal inputs for the proposed fusion network. Different fusion architectures are investigated for fusing the B-mode US image and the local phase features.
The proposed methods was quantitatively and qualitatively evaluated on 546 in vivo scans by scanning 14 healthy subjects. We achieved an average F-score above 95% with an average bone surface localization error of 0.2 mm. The reported results are statistically significant compared to state-of-the-art.
Reported accurate and robust segmentation results make the proposed method promising in CAOS applications. Further extensive validations are required in order to fully understand the clinical utility of the proposed method.
超声（US）提供实时的二维/三维安全成像。由于这些功能，它被认为是各种计算机辅助骨科手术（CAOS）程序中术中荧光透视的安全替代方法。然而，由于噪声水平高、各种成像伪影以及骨表面响应出现几毫米（mm）厚，因此难以解释所采集的骨 US 数据。对于 US 引导的 CAOS 程序，拥有一种强大且计算成本低的分割机制是一个基本目标。
在本文中，我们提出了一种基于卷积神经网络的技术，用于从活体 US 扫描中分割骨表面。我们提出的设计的新颖之处在于它利用特征图融合，并采用多模态图像来减轻由于成像伪影和低强度骨边界引起的变化的敏感性。B 模式 US 图像及其对应的局部相位滤波图像被用作所提出的融合网络的多模态输入。研究了不同的融合架构来融合 B 模式 US 图像和局部相位特征。
该方法在 14 名健康受试者的 546 次活体扫描中进行了定量和定性评估。我们实现了平均 F 分数高于 95%，平均骨表面定位误差为 0.2mm。与最先进的方法相比，报告的结果具有统计学意义。
报告的准确和稳健的分割结果使得该方法在 CAOS 应用中很有前途。为了充分了解所提出方法的临床实用性，需要进行进一步的广泛验证。
