We propose a novel method to automatically calibrate tracked ultrasound probes. To this end we design a custom phantom consisting of nine cones with different heights. The tips are used as key points to be matched between multiple sweeps. We extract them using a convolutional neural network to segment the cones in every ultrasound frame and then track them across the sweep. The calibration is robustly estimated using RANSAC and later refined employing image based techniques. Our phantom can be 3D-printed and offers many advantages over state-of-the-art methods. The phantom design and algorithm code are freely available online. Since our phantom does not require a tracking target on itself, ease of use is improved over currently used techniques. The fully automatic method generalizes to new probes and different vendors, as shown in our experiments. Our approach produces results comparable to calibrations obtained by a domain expert.
翻译:我们建议了一种新颖的自动校准跟踪超声波探测器的方法。 为此, 我们设计了一个由9个高高锥体组成的定制幽灵。 提示是作为多次扫描之间匹配的关键点使用的。 我们利用超声波网络提取它们, 将锥体分解到每个超声波框中, 然后在整个扫描中跟踪它们。 校准是使用RANSAC 进行强力估计的, 并随后使用图像技术加以改进。 我们的幽灵可以打印成3D字, 并且为最新技术提供许多优势。 幽灵设计和算法代码可以在网上自由获取。 由于我们的幻影设计和算法不需要对它本身进行跟踪, 使用起来的方便性会得到改善。 正如我们实验所显示的那样, 完全自动的方法可以对新探测器和不同供应商进行统称。 我们的方法可以产生与域专家获得的校准结果相似的结果 。