Pre- to post-contrast breast MRI synthesis for enhanced tumour segmentation

Richard Osuala; Smriti Joshi; Apostolia Tsirikoglou; Lidia Garrucho; Walter H. L. Pinaya; Oliver Diaz; Karim Lekadir

doi:10.1117/12.3006961

2 April 2024 Pre- to post-contrast breast MRI synthesis for enhanced tumour segmentation

Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Karim Lekadir

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129260Y (2024) https://doi.org/10.1117/12.3006961
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

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

Citation Download Citation

Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, and Karim Lekadir "Pre- to post-contrast breast MRI synthesis for enhanced tumour segmentation", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129260Y (2 April 2024); https://doi.org/10.1117/12.3006961

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KEYWORDS

Image segmentation

Magnetic resonance imaging

Breast

3D image processing

Contrast agents

Adversarial training

Artificial intelligence

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