The University of Electro-Communications, Chofu, Japan.
Nihon University, Tokyo, Japan.
Int J Comput Assist Radiol Surg. 2020 Dec;15(12):1989-1995. doi: 10.1007/s11548-020-02265-1. Epub 2020 Oct 3.
The main purpose of this study is to construct a system to track the tumor position during radiofrequency ablation (RFA) treatment. Existing tumor tracking systems are designed to track a tumor in a two-dimensional (2D) ultrasound (US) image. As a result, the three-dimensional (3D) motion of the organs cannot be accommodated and the ablation area may be lost. In this study, we propose a method for estimating the 3D movement of the liver as a preliminary system for tumor tracking. Additionally, in current 3D movement estimation systems, the motion of different structures during RFA could reduce the tumor visibility in US images. Therefore, we also aim to improve the estimation of the 3D movement of the liver by improving the liver segmentation. We propose a novel approach to estimate the relative 6-axial movement (x, y, z, roll, pitch, and yaw) between the liver and the US probe in order to estimate the overall movement of the liver.
We used a convolutional neural network (CNN) to estimate the 3D displacement from two-dimensional US images. In addition, to improve the accuracy of the estimation, we introduced a segmentation map of the liver region as the input for the regression network. Specifically, we improved the extraction accuracy of the liver region by using a bi-directional convolutional LSTM U-Net with densely connected convolutions (BCDU-Net).
By using BCDU-Net, the accuracy of the segmentation was dramatically improved, and as a result, the accuracy of the movement estimation was also improved. The mean absolute error for the out-of-plane direction was 0.0645 mm/frame.
The experimental results show the effectiveness of our novel method to identify the movement of the liver by BCDU-Net and CNN. Precise segmentation of the liver by BCDU-Net also contributes to enhancing the performance of the liver movement estimation.
本研究的主要目的是构建一个在射频消融（RFA）治疗过程中跟踪肿瘤位置的系统。现有的肿瘤跟踪系统旨在跟踪二维（2D）超声（US）图像中的肿瘤。因此，无法适应器官的三维（3D）运动，并且可能会丢失消融区域。在本研究中，我们提出了一种估计肝脏 3D 运动的方法，作为肿瘤跟踪的初步系统。此外，在当前的 3D 运动估计系统中，RFA 过程中不同结构的运动会降低 US 图像中肿瘤的可见度。因此，我们还旨在通过改善肝脏分割来提高肝脏 3D 运动的估计。我们提出了一种新的方法来估计肝脏和 US 探头之间的相对 6 轴运动（x、y、z、滚转、俯仰和偏航），以便估计肝脏的整体运动。
我们使用卷积神经网络（CNN）从二维 US 图像估计 3D 位移。此外，为了提高估计的准确性，我们引入了肝脏区域的分割图作为回归网络的输入。具体来说，我们通过使用具有密集连接卷积的双向卷积长短期记忆 U-Net（BCDU-Net）来提高肝脏区域的提取精度。
通过使用 BCDU-Net，分割的准确性得到了显著提高，因此运动估计的准确性也得到了提高。离平面方向的平均绝对误差为 0.0645 毫米/帧。
实验结果表明，我们的新方法通过 BCDU-Net 和 CNN 来识别肝脏运动是有效的。BCDU-Net 对肝脏的精确分割也有助于提高肝脏运动估计的性能。
