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Osuala, R. ; Joshi, S.* ; Tsirikoglou, A.* ; Garrucho, L.* ; Pinaya, W.H.L.* ; Diaz, O.* ; Lekadir, K.*

Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation.

In: (Conference on Medical Imaging - Image Processing, 19-22 Februar 2024, San Diego, California). 1000 20th St, Po Box 10, Bellingham, Wa 98227-0010 Usa: SPIE, 2024.:129260Y (Proc. SPIE ; 12926)
DOI
Despite its benefits for tumour detection and treatment, the administration of contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associated with a range of issues, including their invasiveness, bioaccumulation, and a risk of nephrogenic systemic fibrosis. This study explores the feasibility of producing synthetic contrast enhancements by translating pre-contrast T1-weighted fat-saturated breast MRI to their corresponding first DCE-MRI sequence leveraging the capabilities of a generative adversarial network (GAN). Additionally, we introduce a Scaled Aggregate Measure (SAMe) designed for quantitatively evaluating the quality of synthetic data in a principled manner and serving as a basis for selecting the optimal generative model. We assess the generated DCE-MRI data using quantitative image quality metrics and apply them to the downstream task of 3D breast tumour segmentation. Our results highlight the potential of post-contrast DCE-MRI synthesis in enhancing the robustness of breast tumour segmentation models via data augmentation. Our code is available at https://github.com/RichardObi/pre_post_synthesis.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Breast Cancer ; Contrast Agent ; Deep Learning ; Gans ; Generative Models ; Synthetic Data; Image; Cancer
ISSN (print) / ISBN 0277-786X
e-ISSN 1996-756X
Konferenztitel Conference on Medical Imaging - Image Processing
Konferzenzdatum 19-22 Februar 2024
Konferenzort San Diego, California
Zeitschrift Proceedings of SPIE
Quellenangaben Band: 12926, Heft: , Seiten: , Artikelnummer: 129260Y Supplement: ,
Verlag SPIE
Verlagsort 1000 20th St, Po Box 10, Bellingham, Wa 98227-0010 Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
Förderungen Ministry of Science and Innovation of Spain
European Union