Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

Jinwei Zhang; Lianrui Zuo; Blake E. Dewey; Samuel W. Remedios; Savannah P. Hays; Dzung L. Pham; Jerry L. Prince; Aaron Carass

doi:10.1117/12.3011291

2 April 2024 Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Savannah P. Hays, Dzung L. Pham, Jerry L. Prince, Aaron Carass

Author Affiliations +

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 129302I (2024) https://doi.org/10.1117/12.3011291
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Deep learning algorithms using Magnetic Resonance (MR) images have demonstrated state-of-the-art performance in the automated segmentation of Multiple Sclerosis (MS) lesions. Despite their success, these algorithms may fail to generalize across sites or scanners, leading to domain generalization errors. Few-shot or one-shot domain adaptation is an option to reduce the generalization error using limited labeled data from the target domain. However, this approach may not yield satisfactory performance due to the limited data available for adaptation. In this paper, we aim to address this issue by integrating one-shot adaptation data with harmonized training data that includes labels. Our method synthesizes new training data with a contrast similar to that of the test domain, through a process referred to as “contrast harmonization” in MRI. Our experiments show that combining one-shot adaptation data with harmonized training data outperformed the use of either one of the data sources alone. Domain adaptation using only harmonized training data achieved comparable or even better performance compared to one-shot adaptation. In addition, all adaptations only required light fine-tuning of two to five epochs for convergence.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Savannah P. Hays, Dzung L. Pham, Jerry L. Prince, and Aaron Carass "Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 129302I (2 April 2024); https://doi.org/10.1117/12.3011291

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