论文标题
4D时空卷积网络，用于对象位置估计为OCT卷
4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes
论文作者
论文摘要
跟踪和本地化对象是计算机辅助手术中的核心问题。由于其高空间和时间分辨率，光学相干断层扫描（OCT）可用作光学跟踪系统。最近，3D卷积神经网络（CNN）显示了使用单个体积OCT图像对标记对象的姿势估算的有希望的性能。虽然这种方法仅依赖于空间信息，但OCT允许以高体积速率捕获对象的运动的时间流。在这项工作中，我们系统地将3D CNN扩展到4D时空CNN，以评估其他时间信息对标记对象跟踪的影响。在各种体系结构中，我们的结果表明，与使用3D CNN的单个体积处理相比，使用OCT体积流并采用4D时空卷积的平均绝对误差低30％。
Tracking and localizing objects is a central problem in computer-assisted surgery. Optical coherence tomography (OCT) can be employed as an optical tracking system, due to its high spatial and temporal resolution. Recently, 3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single volumetric OCT images. While this approach relied on spatial information only, OCT allows for a temporal stream of OCT image volumes capturing the motion of an object at high volumes rates. In this work, we systematically extend 3D CNNs to 4D spatio-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking. Across various architectures, our results demonstrate that using a stream of OCT volumes and employing 4D spatio-temporal convolutions leads to a 30% lower mean absolute error compared to single volume processing with 3D CNNs.
