  We introduce the idea of inter-slice image augmentation whereby the numbers
of the medical images and the corresponding segmentation labels are increased
between two consecutive images in order to boost medical image segmentation
accuracy. Unlike conventional data augmentation methods in medical imaging,
which only increase the number of training samples directly by adding new
virtual samples using simple parameterized transformations such as rotation,
flipping, scaling, etc., we aim to augment data based on the relationship
between two consecutive images, which increases not only the number but also
the information of training samples. For this purpose, we propose a
frame-interpolation-based data augmentation method to generate intermediate
medical images and the corresponding segmentation labels between two
consecutive images. We train and test a supervised U-Net liver segmentation
network on SLIVER07 and CHAOS2019, respectively, with the augmented training
samples, and obtain segmentation scores exhibiting significant improvement
compared to the conventional augmentation methods.
