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首页> 外文会议>IEEE International Conference on Automation Science and Engineering >Neural-Network-Based Heart Motion Prediction for Ultrasound-Guided Beating-Heart Surgery
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Neural-Network-Based Heart Motion Prediction for Ultrasound-Guided Beating-Heart Surgery

机译：基于神经网络的心脏跳动心脏手术的心脏运动预测

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A neural-network-based heart motion prediction method is proposed for ultrasound-guided beating-heart surgery to compensate for time delays caused by ultrasound (US) image acquisition and processing. Such image processing is needed for tracking heart tissue in US images, which is itself a requirement for beating-heart surgery. Once the heart tissue is tracked in US images, a recurrent neural network (NN) is employed to learn how to predict the motion of the tracked heart motion in order to compensate for the delays introduced in the initial US image processing step. To verify the feasibility of predicting both simple and complex heart motions, the NN is tested with two types of heart motion data: (i) fixed heart rate and maximum amplitude, and (ii) varying heart rate and maximum amplitude. Also, the NN was tested for different prediction horizons and showed effectiveness for both small and large delays. The heart motion prediction results using NN are compared to the results using an extended Kalman filter (EKF) algorithm. Using NN, the mean absolute error and the root mean squared error between the predicted and the actually tracked heart motions are roughly 60% smaller than those achieved by using the EKF. Moreover, the NN is able to predict the heart position up to 1000 ms in advance, which significantly exceeds the typical US image acquisition/processing delays for this application (160 ms in our tests). Overall, the NN predictor shows significant advantages (higher accuracy and longer prediction horizon) compared to the EKF predictor.
机译：提出了一种基于神经网络的心脏运动预测方法，用于超声引导的跳动心脏手术，以补偿由超声（US）图像采集和处理引起的时间延迟。需要这种图像处理来跟踪US图像中的心脏组织，这本身就是心脏跳动手术的要求。一旦在US图像中跟踪了心脏组织，便会使用递归神经网络（NN）来学习如何预测所跟踪的心脏运动的运动，以补偿在初始US图像处理步骤中引入的延迟。为了验证预测简单和复杂心脏运动的可行性，使用两种类型的心脏运动数据测试了NN：（i）固定心率和最大幅度，以及（ii）变化的心率和最大幅度。此外，对NN进行了不同预测范围的测试，并显示了对小延迟和大延迟的有效性。使用NN的心脏运动预测结果与使用扩展卡尔曼滤波器（EKF）算法的结果进行比较。使用NN，预测的和实际跟踪的心脏运动之间的平均绝对误差和均方根误差大约比使用EKF实现的平均误差小60％。而且，NN能够提前1000毫秒预测心脏位置，这大大超过了该应用的典型美国图像采集/处理延迟（在我们的测试中为160毫秒）。总体而言，与EKF预测器相比，NN预测器显示出显着优势（更高的准确性和更长的预测范围）。

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