# Acquisition-invariant brain MRI segmentation with informative uncertainties

arXiv: https://arxiv.org/abs/2111.04094 · HTML: https://mlsanity.com/paper/2111.04094/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 7 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [No New-Net](https://mlsanity.com/paper/1809.10483/) (2018)
- [A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols](https://mlsanity.com/paper/1805.10170/) (2018)
- [A Learning Strategy for Contrast-agnostic MRI Segmentation](https://mlsanity.com/paper/2003.01995/) (2020)
- [Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions](https://mlsanity.com/paper/1806.08640/) (2018)
- [PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation](https://mlsanity.com/paper/1901.05992/) (2019)
- [Physics-informed brain MRI segmentation](https://mlsanity.com/paper/2001.10767/) (2020)
- [Hierarchical brain parcellation with uncertainty](https://mlsanity.com/paper/2009.07573/) (2020)
- [Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis](https://mlsanity.com/paper/2103.02767/) (2021)
- [Unsupervised learning of MRI tissue properties using MRI physics models](https://mlsanity.com/paper/2107.02704/) (2021)

## Built on by (most cited first, 3 of 3)

- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data](https://mlsanity.com/paper/2412.03318/) (2024)
- [Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning](https://mlsanity.com/paper/2501.12057/) (2025)

## Abstract (arXiv)

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.