Acquisition-invariant brain MRI segmentation with informative uncertainties
This work addresses the challenge of combining multi-site medical imaging data for more robust segmentation, which is incremental as it builds on existing methods by incorporating uncertainty modeling and acquisition invariance.
The paper tackles the problem of site-specific biases in multi-site brain MRI segmentation by developing an algorithm that accounts for acquisition effects and models uncertainty, demonstrating generalization to holdout datasets while preserving segmentation quality and enabling harmonization.
Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and therefore any downstream analyses. Post-hoc multi-site correction methods exist but have strong assumptions that often do not hold in real-world scenarios. Algorithms should be designed in a way that can account for site-specific effects, such as those that arise from sequence parameter choices, and in instances where generalisation fails, should be able to identify such a failure by means of explicit uncertainty modelling. This body of work showcases such an algorithm, that can become robust to the physics of acquisition in the context of segmentation tasks, while simultaneously modelling uncertainty. We demonstrate that our method not only generalises to complete holdout datasets, preserving segmentation quality, but does so while also accounting for site-specific sequence choices, which also allows it to perform as a harmonisation tool.