Purpose

Automatic identification of interventional devices in X-ray (XR) fluoroscopy offers the potential of improved navigation during transcatheter endovascular procedures. This paper presents a prototype implementation of fully automatic 3D reconstruction of a cryo-balloon catheter during pulmonary vein isolation (PVI) procedures by deep learning approaches.

Methods

We employ convolutional neural networks (CNN) to automatically identify the cryo-balloon XR marker and catheter shaft in 2D fluoroscopy during PVI. Training data are generated exploiting established semiautomatic techniques, including template-matching and analytical graph building. A first network of U-net architecture uses a single grayscale XR image as input and yields the mask of the XR marker. A second network of the similar architecture is trained using the mask of the XR marker as additional input to the grayscale XR image for the segmentation of the cryo-balloon catheter shaft mask. The structures automatically identified in two 2D images with different angulations are then used to reconstruct the cryo-balloon in 3D.

Results

Automatic identification of the XR marker was successful in 78% of test cases and in 100% for the catheter shaft. Training of the model for prediction of the XR marker mask was successful with 3426 training samples. Incorporation of the XR marker mask as additional input for the model predicting the catheter shaft allowed to achieve good training result with only 805 training samples. The average prediction time per frame was 14.47 ms for the XR marker and 78.22 ms for the catheter shaft. Localization accuracy for the XR marker yielded on average 1.52 pixels or 0.56 mm.

Conclusions

In this paper, we report a novel method for automatic detection and 3D reconstruction of the cryo-balloon catheter shaft and marker from 2D fluoroscopic images. Initial evaluation yields promising results thus indicating the high potential of CNNs as alternatives to the current state-of-the-art solutions.

中文翻译：

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使用 U-net 在 X 射线透视中进行冷冻球囊导管定位

目的

X 射线 (XR) 透视中介入设备的自动识别提供了在经导管血管内手术期间改进导航的潜力。本文介绍了通过深度学习方法在肺静脉隔离 (PVI) 过程中对冷冻球囊导管进行全自动 3D 重建的原型实现。

方法

我们采用卷积神经网络 (CNN) 在 PVI 期间自动识别 2D 透视中的冷冻球囊 XR 标记和导管轴。训练数据是利用已建立的半自动技术生成的，包括模板匹配和分析图构建。U-net 架构的第一个网络使用单个灰度 XR 图像作为输入并产生 XR 标记的掩码。使用 XR 标记的掩码作为灰度 XR 图像的附加输入来训练类似架构的第二个网络，以分割冷冻球囊导管轴掩码。然后使用具有不同角度的两个 2D 图像中自动识别的结构来重建 3D 冷冻气球。

结果

XR 标记的自动识别在 78% 的测试案例和 100% 的导管轴中成功。使用 3426 个训练样本成功训练了用于预测 XR 标记掩码的模型。将 XR 标记面罩作为预测导管轴的模型的附加输入，仅使用 805 个训练样本即可实现良好的训练结果。XR 标记的每帧平均预测时间为 14.47 毫秒，导管轴为 78.22 毫秒。XR 标记的定位精度平均为 1.52 像素或 0.56 毫米。

结论

在本文中，我们报告了一种从 2D 透视图像自动检测和 3D 重建冷冻球囊导管轴和标记的新方法。初步评估产生了有希望的结果，因此表明 CNN 作为当前最先进解决方案的替代品具有很高的潜力。