3D Manifold Topology Based Medical Image Data Augmentation

Abstract

Data augmentation is an effective and universal technique for improving the generalization performance of deep neural networks. Current data augmentation implementations usually involve geometric and photometric transformations. However, none of them considers the topological information in images, which is an important global invariant of the three-dimensional manifold. In our implementation, we design a novel method that finds the generator of the first homology group, i.e. closed loops cannot shrink to a point, of 3D image and erases the bounding box of a random loop. To the best of our knowledge, it is the first time that data augmentation based on the first homology group of the three-dimensional image is applied in medical image augmentation. Our numerical experiments demonstrate that the proposed approach outperforms the state-of-the-art method.