  Due to the limitation of available labeled data, medical image segmentation
is a challenging task for deep learning. Traditional data augmentation
techniques have been shown to improve segmentation network performances by
optimizing the usage of few training examples. However, current augmentation
approaches for segmentation do not tackle the strong texture bias of
convolutional neural networks, observed in several studies. This work shows on
the MoNuSeg dataset that style augmentation, which is already used in
classification tasks, helps reducing texture over-fitting and improves
segmentation performance.
