论文标题
通过基于流体的图像登记的解剖学数据增强
Anatomical Data Augmentation via Fluid-based Image Registration
论文作者
论文摘要
我们引入了一种基于流体的图像增强方法，以进行医学图像分析。与现有方法相反，我们的框架通过从给定样品的基础地理子空间插值生成解剖上有意义的图像。我们的方法由三个步骤组成：1）给定源图像和一组目标图像，我们使用大变形差异度量映射（LDDMM）模型构建了一个测量子空间； 2）我们从所得的测量子空间中采样转换； 3）我们通过插值获得变形的图像和分割。大脑（LPBA）和膝盖（OAI）数据的实验说明了我们在两项任务上的方法的性能：1）在训练和测试图像分割过程中的数据增加； 2）单图像分割的单次学习。我们证明我们的方法会产生解剖学上有意义的数据，并在竞争方法上提高这些任务的性能。代码可在https://github.com/uncbiag/easyreg上找到。
We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our approach consists of three steps: 1) given a source image and a set of target images, we construct a geodesic subspace using the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model; 2) we sample transformations from the resulting geodesic subspace; 3) we obtain deformed images and segmentations via interpolation. Experiments on brain (LPBA) and knee (OAI) data illustrate the performance of our approach on two tasks: 1) data augmentation during training and testing for image segmentation; 2) one-shot learning for single atlas image segmentation. We demonstrate that our approach generates anatomically meaningful data and improves performance on these tasks over competing approaches. Code is available at https://github.com/uncbiag/easyreg.
