Acquisition-invariant brain MRI segmentation with informative uncertainties

TL;DR

This paper introduces a novel brain MRI segmentation algorithm that is robust to site-specific acquisition effects and models uncertainty, enabling better generalization across multi-site datasets and serving as a harmonization tool.

Contribution

The proposed method explicitly accounts for acquisition site effects and uncertainty, improving multi-site MRI segmentation robustness and generalization beyond existing correction techniques.

Findings

Method generalizes well to holdout datasets

Preserves segmentation quality across sites

Acts as an effective harmonization tool

Abstract

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.