# Quantitative Metrics for Benchmarking Medical Image Harmonization

arXiv: https://arxiv.org/abs/2402.04426 · HTML: https://mlsanity.com/paper/2402.04426/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model](https://mlsanity.com/paper/1804.01947/) (2018)
- [Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN](https://mlsanity.com/paper/1809.04536/) (2018)
- [MR-Contrast-Aware Image-to-Image Translations with Generative Adversarial Networks](https://mlsanity.com/paper/2104.01449/) (2021)

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

- [Unpaired Volumetric Harmonization of Brain MRI with Conditional Latent Diffusion](https://mlsanity.com/paper/2408.09315/) (2024)
- [Harmonizing Flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization](https://mlsanity.com/paper/2407.15717/) (2024)
- [Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration](https://mlsanity.com/paper/2407.13632/) (2024)
- [Unified Multi-Site Multi-Sequence Brain MRI Harmonization Enriched by Biomedical Semantic Style](https://mlsanity.com/paper/2601.08193/) (2026)
- [FeTTL: Federated Template and Task Learning for Multi-Institutional Medical Imaging](https://mlsanity.com/paper/2601.16302/) (2026)
- [Task-Adaptive 3D Cross-Field MRI Translation via Field-Conditioned Content-Style Pretraining](https://mlsanity.com/paper/2608.09264/) (2026)

## Abstract (arXiv)

Image harmonization is an important preprocessing strategy to address domain shifts arising from data acquired using different machines and scanning protocols in medical imaging. However, benchmarking the effectiveness of harmonization techniques has been a challenge due to the lack of widely available standardized datasets with ground truths. In this context, we propose three metrics: two intensity harmonization metrics and one anatomy preservation metric for medical images during harmonization, where no ground truths are required. Through extensive studies on a dataset with available harmonization ground truth, we demonstrate that our metrics are correlated with established image quality assessment metrics. We show how these novel metrics may be applied to real-world scenarios where no harmonization ground truth exists. Additionally, we provide insights into different interpretations of the metric values, shedding light on their significance in the context of the harmonization process. As a result of our findings, we advocate for the adoption of these quantitative harmonization metrics as a standard for benchmarking the performance of image harmonization techniques.