  Deep networks are now ubiquitous in large-scale multi-center imaging studies.
However, the direct aggregation of images across sites is contraindicated for
downstream statistical and deep learning-based image analysis due to
inconsistent contrast, resolution, and noise. To this end, in the absence of
paired data, variations of Cycle-consistent Generative Adversarial Networks
have been used to harmonize image sets between a source and target domain.
Importantly, these methods are prone to instability, contrast inversion,
intractable manipulation of pathology, and steganographic mappings which limit
their reliable adoption in real-world medical imaging. In this work, based on
an underlying assumption that morphological shape is consistent across imaging
sites, we propose a segmentation-renormalized image translation framework to
reduce inter-scanner heterogeneity while preserving anatomical layout. We
replace the affine transformations used in the normalization layers within
generative networks with trainable scale and shift parameters conditioned on
jointly learned anatomical segmentation embeddings to modulate features at
every level of translation. We evaluate our methodologies against recent
baselines across several imaging modalities (T1w MRI, FLAIR MRI, and OCT) on
datasets with and without lesions. Segmentation-renormalization for translation
GANs yields superior image harmonization as quantified by Inception distances,
demonstrates improved downstream utility via post-hoc segmentation accuracy,
and improved robustness to translation perturbation and self-adversarial
attacks.
