# Physics-informed brain MRI segmentation

arXiv: https://arxiv.org/abs/2001.10767 · HTML: https://mlsanity.com/paper/2001.10767/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [MR-Contrast-Aware Image-to-Image Translations with Generative Adversarial Networks](https://mlsanity.com/paper/2104.01449/) (2021)
- [PINNs for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2408.01026/) (2024)
- [Acquisition-invariant brain MRI segmentation with informative uncertainties](https://mlsanity.com/paper/2111.04094/) (2021)
- [Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials](https://mlsanity.com/paper/2502.14184/) (2025)
- [The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties](https://mlsanity.com/paper/2111.02771/) (2021)

## Abstract (arXiv)

Magnetic Resonance Imaging (MRI) is one of the most flexible and powerful medical imaging modalities. This flexibility does however come at a cost; MRI images acquired at different sites and with different parameters exhibit significant differences in contrast and tissue appearance, resulting in downstream issues when quantifying brain anatomy or the presence of pathology. In this work, we propose to combine multiparametric MRI-based static-equation sequence simulations with segmentation convolutional neural networks (CNN), to make these networks robust to variations in acquisition parameters. Results demonstrate that, when given both the image and their associated physics acquisition parameters, CNNs can produce segmentations that exhibit robustness to acquisition variations. We also show that the proposed physics-informed methods can be used to bridge multi-centre and longitudinal imaging studies where imaging acquisition varies across a site or in time.