论文标题
与灰色价值不变网络的跨模态学习的随机平滑灰色价值转换
Random smooth gray value transformations for cross modality learning with gray value invariant networks
论文作者
论文摘要
随机转换通常用于增强训练数据，目的是降低训练样本的均匀性。这些转换通常针对从相同模态的图像中可以预期的变化。在这里，我们提出了一种简单的方法来转换图像的灰色值，以减少交叉模态差异。这种方法可以使用专门用MR图像训练的网络对CT图像中的腰椎体进行分割。源代码可在https://github.com/nlessmann/rsgt上找到
Random transformations are commonly used for augmentation of the training data with the goal of reducing the uniformity of the training samples. These transformations normally aim at variations that can be expected in images from the same modality. Here, we propose a simple method for transforming the gray values of an image with the goal of reducing cross modality differences. This approach enables segmentation of the lumbar vertebral bodies in CT images using a network trained exclusively with MR images. The source code is made available at https://github.com/nlessmann/rsgt
