论文标题
通过快速和通用样式转移删除超声图像分割的外观移动
Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer
论文作者
论文摘要
Deep Neural Networks (DNNs) suffer from the performance degradation when image appearance shift occurs, especially in ultrasound (US) image segmentation.在本文中，我们提出了一个新颖而直观的框架来消除外观变化，从而提高了DNN的普遍性能力。我们的工作有三个亮点。首先，我们遵循普遍风格转移的精神去除外观变化，这是我们图像之前没有探索的。在不牺牲图像结构细节的情况下，它可以实现任意风格的传输。其次，随着自适应实例归一化块的加速，我们的框架实现了美国临床扫描所需的实时速度。 Third, an efficient and effective style image selection strategy is proposed to ensure the target-style US image and testing content US image properly match each other. Experiments on two large US datasets demonstrate that our methods are superior to state-of-the-art methods on making DNNs robust against various appearance shifts.
Deep Neural Networks (DNNs) suffer from the performance degradation when image appearance shift occurs, especially in ultrasound (US) image segmentation. In this paper, we propose a novel and intuitive framework to remove the appearance shift, and hence improve the generalization ability of DNNs. Our work has three highlights. First, we follow the spirit of universal style transfer to remove appearance shifts, which was not explored before for US images. Without sacrificing image structure details, it enables the arbitrary style-content transfer. Second, accelerated with Adaptive Instance Normalization block, our framework achieved real-time speed required in the clinical US scanning. Third, an efficient and effective style image selection strategy is proposed to ensure the target-style US image and testing content US image properly match each other. Experiments on two large US datasets demonstrate that our methods are superior to state-of-the-art methods on making DNNs robust against various appearance shifts.
