The LUMirage: An independent evaluation of zero-shot performance in the LUMIR challenge

TL;DR

This paper critically re-evaluates the zero-shot performance claims of deep learning methods in neuroimaging registration, revealing limitations on out-of-distribution data, scalability issues, and sensitivity to preprocessing, thus emphasizing the need for realistic evaluation protocols.

Contribution

It provides an independent, rigorous assessment of zero-shot generalization in neuroimaging registration, challenging optimistic claims and highlighting practical limitations of deep learning methods.

Findings

Deep learning methods perform well on in-distribution T1w images and macaque data.

Performance drops significantly on out-of-distribution contrasts like T2 and FLAIR.

Deep learning methods struggle with high-resolution images and are sensitive to preprocessing choices.

Abstract

The LUMIR challenge represents an important benchmark for evaluating deformable image registration methods on large-scale neuroimaging data. While the challenge demonstrates that modern deep learning methods achieve competitive accuracy on T1-weighted MRI, it also claims exceptional zero-shot generalization to unseen contrasts and resolutions, assertions that contradict established understanding of domain shift in deep learning. In this paper, we perform an independent re-evaluation of these zero-shot claims using rigorous evaluation protocols while addressing potential sources of instrumentation bias. Our findings reveal a more nuanced picture: (1) deep learning methods perform comparably to iterative optimization on in-distribution T1w images and even on human-adjacent species (macaque), demonstrating improved task understanding; (2) however, performance degrades significantly on out-of-distribution contrasts (T2, T2*, FLAIR), with Cohen's d scores ranging from 0.7-1.5, indicating substantial practical impact on downstream clinical workflows; (3) deep learning methods face scalability limitations on high-resolution data, failing to run on 0.6 mm isotropic images, while iterative methods benefit from increased resolution; and (4) deep methods exhibit high sensitivity to preprocessing choices. These results align with the well-established literature on domain shift and suggest that claims of universal zero-shot superiority require careful scrutiny. We advocate for evaluation protocols that reflect practical clinical and research workflows rather than conditions that may inadvertently favor particular method classes.