  \textit{Objectives}: Data scarcity and domain shifts lead to biased training
sets that do not accurately represent deployment conditions. A related
practical problem is cross-modal image segmentation, where the objective is to
segment unlabelled images using previously labelled datasets from other imaging
modalities. \textit{Methods}: We propose a cross-modal segmentation method
based on conventional image synthesis boosted by a new data augmentation
technique called Generative Blending Augmentation (GBA). GBA leverages a SinGAN
model to learn representative generative features from a single training image
to diversify realistically tumor appearances. This way, we compensate for image
synthesis errors, subsequently improving the generalization power of a
downstream segmentation model. The proposed augmentation is further combined to
an iterative self-training procedure leveraging pseudo labels at each pass.
\textit{Results}: The proposed solution ranked first for vestibular schwannoma
(VS) segmentation during the validation and test phases of the MICCAI CrossMoDA
2022 challenge, with best mean Dice similarity and average symmetric surface
distance measures. \textit{Conclusion and significance}: Local contrast
alteration of tumor appearances and iterative self-training with pseudo labels
are likely to lead to performance improvements in a variety of segmentation
contexts.
