论文标题
超声弹性学中光流卷卷神经网络的半监督训练
Semi-Supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography
论文作者
论文摘要
卷积神经网络（CNN）已被发现在光流问题中具有巨大的潜力，这要归功于训练深网的大量数据。超声弹性图（使用）中的位移估计步骤可以看作是光流问题。尽管CNN在光学流中的性能高，但由于输入和使用网络的输出都施加了独特的挑战，因此很少使用它们用于使用。与自然图像相比，超声数据具有更高的高频含量。输出也截然不同，其中使用中的位移值通常没有明显的动作或不连续性。一般趋势目前是使用预训练的网络，并在小型仿真超声数据库中微调它们。但是，现实的超声模拟在计算上很昂贵。同样，模拟技术不会对超声成像中的复杂运动，非线性和频率依赖性声学以及许多人工制品来源进行建模。本文中，我们提出了一种无监督的微调技术，使我们能够使用一个大型未标记的数据集来微调CNN光流网络。我们表明，所提出的无监督的微调方法显着改善了网络的性能，并减少了在计算机视觉数据库中训练的网络生成的工件。
Convolutional Neural Networks (CNN) have been found to have great potential in optical flow problems thanks to an abundance of data available for training a deep network. The displacement estimation step in UltraSound Elastography (USE) can be viewed as an optical flow problem. Despite the high performance of CNNs in optical flow, they have been rarely used for USE due to unique challenges that both input and output of USE networks impose. Ultrasound data has much higher high-frequency content compared to natural images. The outputs are also drastically different, where displacement values in USE are often smooth without sharp motions or discontinuities. The general trend is currently to use pre-trained networks and fine-tune them on a small simulation ultrasound database. However, realistic ultrasound simulation is computationally expensive. Also, the simulation techniques do not model complex motions, nonlinear and frequency-dependent acoustics, and many sources of artifact in ultrasound imaging. Herein, we propose an unsupervised fine-tuning technique which enables us to employ a large unlabeled dataset for fine-tuning of a CNN optical flow network. We show that the proposed unsupervised fine-tuning method substantially improves the performance of the network and reduces the artifacts generated by networks trained on computer vision databases.
