mlsanityData to 13 Oct 2026

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

arXiv 2311.10879 ↗First posted 17 Nov 2023eess.IV cs.CV cs.LGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). Builds on 12 of its 40 references that are in the map. First posted on arXiv 17 Nov 2023.

Builds on

12 of 40 references.

References · 12

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,321
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,082
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,457
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,516
  6. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,178
  7. 2016Least Squares Generative Adversarial Networkscited by 1,000
  8. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 123
  9. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
  10. 2022medigan: a Python library of pretrained generative models for medical image synthesiscited by 14
  11. 2023Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRIcited by 11
  12. 2023Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanismcited by 8

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 4.

9 papers.

  1. 2024A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentationscited by 40
  2. 2024Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Modelscited by 11
  3. 2024Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networkscited by 6
  4. 2026MRI Contrast Enhancement Kinetics World Modelcited by 4
  5. 2025StructDiff: Structure-aware Diffusion Model for 3D Fine-grained Medical Image Synthesiscited by 0
  6. 2025HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Modelscited by 0
  7. 2026Pre- to Post-Contrast Synthesis of Breast DCE-MRI using Latent Bridge Matchingcited by 0
  8. 2026Anatomy-Aware Synthesis of Post-Contrast Breast MRI from Pre-Contrast Imagescited by 0
  9. 2026Anguinus Sculpturae: Compositional Synthesis of Peak-Enhancement Breast DCE-MRI Scanscited by 0