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Combining multi-site data can strengthen and uncover trends, but is a taskthat is marred by the influence of site-specific covariates that can bias thedata and therefore any downstream analyses. Post-hoc multi-site correctionmethods exist but have strong assumptions that often do not hold in real-worldscenarios. Algorithms should be designed in a way that can account forsite-specific effects, such as those that arise from sequence parameterchoices, and in instances where generalisation fails, should be able toidentify such a failure by means of explicit uncertainty modelling. This bodyof work showcases such an algorithm, that can become robust to the physics ofacquisition in the context of segmentation tasks, while simultaneouslymodelling uncertainty. We demonstrate that our method not only generalises tocomplete holdout datasets, preserving segmentation quality, but does so whilealso accounting for site-specific sequence choices, which also allows it toperform as a harmonisation tool.

Acquisition-invariant brain MRI segmentation with informative uncertainties