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Novel multimodal imaging methods are capable of generating extensive, superhigh resolution datasets for preclinical research. Yet, a massive lack ofannotations prevents the broad use of deep learning to analyze such data. Sofar, existing generative models fail to mitigate this problem because offrequent labeling errors. In this paper, we introduce a novel generative methodwhich leverages real anatomical information to generate realistic image-labelpairs of tumours. We construct a dual-pathway generator, for the anatomicalimage and label, trained in a cycle-consistent setup, constrained by anindependent, pretrained segmentor. The generated images yield significantquantitative improvement compared to existing methods. To validate the qualityof synthesis, we train segmentation networks on a dataset augmented with thesynthetic data, substantially improving the segmentation over baseline.

METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy