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首页> 外文期刊>Current Directions in Biomedical Engineering >Intraoperative Registration of 2D C-arm Images with Preoperative CT Data in Computer Assisted Spine Surgery: Motivation to Use Convolutional Neural Networks for Initial Pose Generator : Current Directions in Biomedical Engineering
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Intraoperative Registration of 2D C-arm Images with Preoperative CT Data in Computer Assisted Spine Surgery: Motivation to Use Convolutional Neural Networks for Initial Pose Generator : Current Directions in Biomedical Engineering

机译：在计算机辅助脊柱外科手术中用术前CT数据对2D C型臂图像进行术中配准：使用卷积神经网络作为初始姿势发生器的动机：生物医学工程学的当前方向

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In this paper, we present an approach for getting an initial pose to use in a 2D/3D registration process for computer-assisted spine surgery. This is an iterative process that requires an initial pose close to the actual final pose. When using a proper initial pose, we get registrations within two millimeters of accuracy. Consequently, we developed a fully connected neural network (FCNN), which predicts the pose of a specific 2D image within an acceptable range. Therefore, we can use this result as the initial pose for the registration process. However, the inability of the FCNN for learning spatial attributes, and the decrease of the resolution of the images before inserting them in the FCNN, make the variance of the prediction large enough to make some of the predictions entirely out of the acceptable range. Additionally, new researches in deep learning field have shown that convolutional neural networks (CNNs) offer high advantages when the inputs of the net are images. We consider that using CNNs can help to improve our results, generalizing the system for a greater variety of inputs, and facilitating the integration with our current workflow. Then we present an outline for a CNN for our application, and some further steps we need to complete to achieve this implementation.
机译：在本文中，我们提出一种在计算机辅助脊柱手术的2D / 3D注册过程中使用初始姿势的方法。这是一个迭代过程，需要一个初始姿势接近实际最终姿势。当使用正确的初始姿势时，我们获得的定位精度在2毫米以内。因此，我们开发了一个完全连接的神经网络（FCNN），可以预测特定2D图像在可接受范围内的姿态。因此，我们可以将此结果用作注册过程的初始姿势。但是，FCNN无法学习空间属性，并且在将图像插入FCNN之前降低了图像的分辨率，这使得预测的方差足够大，使得某些预测完全超出了可接受的范围。此外，深度学习领域的新研究表明，当网络的输入为图像时，卷积神经网络（CNN）具有很高的优势。我们认为使用CNN可以帮助改善我们的结果，为更多输入提供通用化的系统，并促进与当前工作流程的集成。然后，我们为我们的应用程序提供了CNN的概述，以及完成该实现所需的其他一些步骤。

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