Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
arXiv:2311.10879 · doi:10.1117/12.3006961
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.
Accepted as oral presentation at SPIE Medical Imaging 2024 (Image Processing)
References in corpus (5)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Automated Design of Deep Learning Methods for Biomedical Image Segmentation
- Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging
- medigan: a Python library of pretrained generative models for medical image synthesis
- Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism