Quantitative Metrics for Benchmarking Medical Image Harmonization
Abhijeet Parida Zhifan Jiang Roger J. Packer Robert A. Avery Syed M. Anwar Marius G. Linguraru
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.