mlsanityData to 14 Oct 2026

Evaluation of 3D Counterfactual Brain MRI Generation

arXiv 2508.02880 ↗First posted 4 Aug 2025eess.IV cs.CVcited by 2

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Abstract (arXiv)

Counterfactual generation offers a principled framework for simulating hypothetical changes in medical imaging, with potential applications in understanding disease mechanisms and generating physiologically plausible data. However, generating realistic structural 3D brain MRIs that respect anatomical and causal constraints remains challenging due to data scarcity, structural complexity, and the lack of standardized evaluation protocols. In this work, we convert six generative models into 3D counterfactual approaches by incorporating an anatomy-guided framework based on a causal graph, in which regional brain volumes serve as direct conditioning inputs. Each model is evaluated with respect to composition, reversibility, realism, effectiveness and minimality on T1-weighted brain MRIs (T1w MRIs) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In addition, we test the generalizability of each model with respect to T1w MRIs of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). Our results indicate that anatomically grounded conditioning successfully modifies the targeted anatomical regions; however, it exhibits limitations in preserving non-targeted structures. Beyond laying the groundwork for more interpretable and clinically relevant generative modeling of brain MRIs, this benchmark highlights the need for novel architectures that more accurately capture anatomical interdependencies.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 4 Aug 2025.

Builds on

15 of 24 references.

References · 15

  1. 2016Ladder Variational Autoencoderscited by 318
  2. 2019Med3D: Transfer Learning for 3D Medical Image Analysiscited by 288
  3. 2020Hierarchical Amortized Training for Memory-efficient High Resolution 3D GANcited by 60
  4. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  5. 2022Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasetscited by 51
  6. 2019Learning to synthesise the ageing brain without longitudinal datacited by 42
  7. 2023Measuring axiomatic soundness of counterfactual image modelscited by 34
  8. 2020Longitudinal Self-Supervised Learningcited by 30
  9. 2023BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeyscited by 29
  10. 2022Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Modelcited by 28
  11. 2024Benchmarking Counterfactual Image Generationcited by 22
  12. 2024Deep Generative Models for 3D Medical Image Synthesiscited by 14
3 more
  1. 2023Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIscited by 12
  2. 2024Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencodercited by 10
  3. 2019Semi-supervised Image Attribute Editing using Generative Adversarial Networkscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Integrating Anatomical Priors into a Causal Diffusion Modelcited by 1
  2. 2026A Neuroimaging Simulation Framework for Developing and Evaluating Causal AIcited by 0