bibtext: @InProceedings{ Esh_ESPA_MICCAI2024,
                   author = { Eshaghzadeh Torbati, Mahbaneh and Minhas, Davneet S. and Tafti, Ahmad P. and DeCarli, Charles S. and Tudorascu, Dana L. and Hwang, Seong Jae },
                   title = { { ESPA: An Unsupervised Harmonization Framework via Enhanced Structure Preserving Augmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15002 },
                   month = {October},
                   pages = { pending },
                }
abstract: The rising interest in pooling neuroimaging data from various sources presents challenges regarding scanner variability, known as scanner effects. While numerous harmonization methods aim to tackle these effects, they face issues with model robustness, brain structural modifications, and over-correction. To combat these issues, we propose a novel harmonization approach centered on simulating scanner effects through augmentation methods. This strategy enhances model robustness by providing extensive simulated matched data, comprising sets of images with similar brain but varying scanner effects. Our proposed method, ESPA, is an unsupervised harmonization framework via Enhanced Structure Preserving Augmentation. Additionally, we introduce two domain-adaptation augmentation: tissue-type contrast augmentation and GAN-based residual augmentation, both focusing on appearancebased changes to address structural modifications. While the former adapts images to the tissue-type contrast distribution of a target scanner, the latter generates residuals added to the original image for more complex scanner adaptation. These augmentations assist ESPA in mitigating over correction through data stratification or population matching strategies during augmentation configuration. Notably, we leverage our unique in-house matched dataset as a benchmark to compare ESPA against supervised and unsupervised state-of-the art (SOTA) harmonization methods. Our study marks the first attempt, to the best of our knowledge, to address harmonization by simulating scanner effects. Our results demonstrate the successful simulation of scanner effects, with ESPA outperforming SOTA methods using this harmonization approach.
title: ESPA: An Unsupervised Harmonization Framework via Enhanced Structure Preserving Augmentation
authors: Eshaghzadeh Torbati, Mahbaneh Minhas, Davneet S. Tafti, Ahmad P. DeCarli, Charles S. Tudorascu, Dana L. Hwang, Seong Jae