3D augmentation for volumetric whole heart segmentation

Abstract

Abstract Data augmentation is a technique usually deployed to mitigate the possible performance limitation from training a neural network model on a limited dataset, especially in the medical domain. This paper presents a study on effects of applying different rotation settings to augment cardiac volumes from the Multi-modality Whole Heart Segmentation dataset, in order to improve the segmentation performance. This study presents a comparison between conventional 2D (slice-wise) rotation primarily on the axial axis, 3D (volume-wise) rotation, and our proposed rotation setting that takes into account possible cardiac alignment according to its anatomy. The study has suggested two key considerations: 2D slice-wise rotation should be avoided when using 3D data for segmentation, due to intrinsic structural correlation between subsequent slices, and that 3D rotations may help improve segmentation performance on data previously unseen to the model.

Keywords

• Data augmentation
• Convolutional neural networks
• Cardiac segmentation
• Volumetric data