# name
Fat-suppressed breast MRI synthesis for domain adaptation in tumour segmentation

# metadata.dc.description.abstract[].value
Heterogeneity in dynamic contrast-enhanced breast MRI acquisition protocols hinders the generalization of automatic tumour segmentation tools. While fat-suppressed MRI acquisition is common, some vendors do not provide these sequences, making a segmentation model trained with fat-suppressed images unusable for non-fat-suppressed cases. In this study, we propose two strategies to alleviate this issue. The first approach involves translating non-fat-suppressed to fat-suppressed breast MRI. The second approach integrates synthetic non-fat-suppressed MRI into the training pipeline of tumour segmentation models. Our experimental results demonstrate that both approaches significantly improve segmentation performance on non-fat-suppressed MRI, suggesting that domain adaptation techniques based on image synthesis can enhance the accuracy and reliability of tumour segmentation in breast MRI. The generative models will be made publicly available at medigan library (medigan [18] GitHub repository).

# metadata.dc.relation.isformatof[].value
Versió postprint de la comunicació Fat-suppressed breast MRI synthesis for domain adaptation in tumour segmentation del volum publicat a: https://doi.org/10.1007/978-3-031-77789-9_20

# metadata.dc.relation.ispartof[].value
Comunicació al congrés: Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care: First Deep Breast Workshop, Deep-Breath 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024.

# metadata.dc.rights[].value
Springer Nature Switzerland AG (c) Lídia Garrucho et al., 2025

# metadata.dc.source[].value
Comunicacions a congressos  (Matemàtiques i Informàtica)

# metadata.dc.title[].value
Fat-suppressed breast MRI synthesis for domain adaptation in tumour segmentation