Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866916267074519040 |
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| author | Osuala, Richard Joshi, Smriti Tsirikoglou, Apostolia Garrucho, Lidia Pinaya, Walter H. L. Diaz, Oliver Lekadir, Karim |
| author_facet | Osuala, Richard Joshi, Smriti Tsirikoglou, Apostolia Garrucho, Lidia Pinaya, Walter H. L. Diaz, Oliver Lekadir, Karim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_10879 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation Osuala, Richard Joshi, Smriti Tsirikoglou, Apostolia Garrucho, Lidia Pinaya, Walter H. L. Diaz, Oliver Lekadir, Karim Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2311.10879 |