论文标题
通过从原始光谱OCT数据深度学习来估算针头力量
Needle tip force estimation by deep learning from raw spectral OCT data
论文作者
论文摘要
目的。对于活检或近距离放射治疗等应用，针头放置是一个具有挑战性的问题。尖端力传感可以为组织内部的针线导航提供有价值的反馈。为此，可以将光纤传感器直接集成到针尖中。光学相干断层扫描（OCT）可用于图像组织。在这里，我们研究了如何将OCT校准为角色，例如在机器针放置期间。 方法。我们研究使用原始光谱OCT数据而不典型的图像重建是否可以改善光学信号和力之间的深度学习校准。为此，我们考虑了使用卷积神经网络（CNN）进行校准的新的，更健壮的设计的三个不同针头。我们将训练CNN与原始OCT信号和重建深度曲线进行比较。 结果。我们发现，使用原始数据作为最大的CNN模型的输入优于使用重建数据，而平均绝对误差为5.81 MN，而8.04 MN的使用平均误差为5.81 MN。 结论。我们发现，使用原始光谱OCT数据进行深度学习可以改善力估计任务的学习。我们的针头设计和校准方法构成了一个非常准确的光纤传感器，用于测量针尖处的力。
Purpose. Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside the tissue. For this purpose, fiber-optical sensors can be directly integrated into the needle tip. Optical coherence tomography (OCT) can be used to image tissue. Here, we study how to calibrate OCT to sense forces, e.g. during robotic needle placement. Methods. We investigate whether using raw spectral OCT data without a typical image reconstruction can improve a deep learning-based calibration between optical signal and forces. For this purpose, we consider three different needles with a new, more robust design which are calibrated using convolutional neural networks (CNNs). We compare training the CNNs with the raw OCT signal and the reconstructed depth profiles. Results. We find that using raw data as an input for the largest CNN model outperforms the use of reconstructed data with a mean absolute error of 5.81 mN compared to 8.04 mN. Conclusions. We find that deep learning with raw spectral OCT data can improve learning for the task of force estimation. Our needle design and calibration approach constitute a very accurate fiber-optical sensor for measuring forces at the needle tip.
