  Supervised training a deep neural network aims to "teach" the network to
mimic human visual perception that is represented by image-and-label pairs in
the training data. Superpixelized (SP) images are visually perceivable to
humans, but a conventionally trained deep learning model often performs poorly
when working on SP images. To better mimic human visual perception, we think it
is desirable for the deep learning model to be able to perceive not only raw
images but also SP images. In this paper, we propose a new superpixel-based
data augmentation (SPDA) method for training deep learning models for
biomedical image segmentation. Our method applies a superpixel generation
scheme to all the original training images to generate superpixelized images.
The SP images thus obtained are then jointly used with the original training
images to train a deep learning model. Our experiments of SPDA on four
biomedical image datasets show that SPDA is effective and can consistently
improve the performance of state-of-the-art fully convolutional networks for
biomedical image segmentation in 2D and 3D images. Additional studies also
demonstrate that SPDA can practically reduce the generalization gap.
