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Garrucho, L.* ; Delegue, E.* ; Osuala, R. ; Kessler, D.* ; Kushibar, K.* ; Diaz, O.* ; Lekadir, K.* ; Igual, L.*

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

In: (Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care). Berlin [u.a.]: Springer, 2025. 202-211 (Lect. Notes Comput. Sc. ; 15451 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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).
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Breast Cancer ; Deep Learning ; Fat Suppression ; Generative Models ; Image-to-image Translation ; Synthetic Data
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15451 LNCS, Heft: , Seiten: 202-211 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
Förderungen Juan de la Cierva fellowship
AGAUR
MICINN
Spain's Ministry of Science and Innovation
EU
DOI 10.1007/978-3-031-77789-9_20
WOS ID 001544124300020
Scopus ID 85219187011
Erfassungsdatum 2025-05-06
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