mlsanityData to 7 Oct 2026

CF-Seg: Counterfactuals meet Segmentation

arXiv 2506.16213 ↗First posted 19 Jun 2025eess.IV cs.AI cs.CVcited by 10

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Abstract (arXiv)

Segmenting anatomical structures in medical images plays an important role in the quantitative assessment of various diseases. However, accurate segmentation becomes significantly more challenging in the presence of disease. Disease patterns can alter the appearance of surrounding healthy tissues, introduce ambiguous boundaries, or even obscure critical anatomical structures. As such, segmentation models trained on real-world datasets may struggle to provide good anatomical segmentation, leading to potential misdiagnosis. In this paper, we generate counterfactual (CF) images to simulate how the same anatomy would appear in the absence of disease without altering the underlying structure. We then use these CF images to segment structures of interest, without requiring any changes to the underlying segmentation model. Our experiments on two real-world clinical chest X-ray datasets show that the use of counterfactual images improves anatomical segmentation, thereby aiding downstream clinical decision-making.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 19 Jun 2025.

Builds on

22 of 36 references.

References · 22

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2019PadChest: A large chest x-ray image dataset with multi-label annotated reportscited by 308
  3. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 210
  4. 2017CausalGAN: Learning Causal Implicit Generative Models with Adversarial Trainingcited by 152
  5. 2017Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentationcited by 109
  6. 2022Diffusion Causal Models for Counterfactual Estimationcited by 87
  7. 2022Neural Causal Models for Counterfactual Identification and Estimationcited by 54
  8. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  9. 2020Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoderscited by 38
  10. 2021Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative modelscited by 36
  11. 2024Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledgecited by 35
  12. 2023CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray imagescited by 33
10 more
  1. 2020Pseudo-healthy synthesis with pathology disentanglement and adversarial learningcited by 19
  2. 2019Shape-Aware Complementary-Task Learning for Multi-Organ Segmentationcited by 15
  3. 2023Many tasks make light work: Learning to localise medical anomalies from multiple synthetic taskscited by 12
  4. 2023Debiasing Counterfactuals In the Presence of Spurious Correlationscited by 12
  5. 2024Counterfactual contrastive learning: robust representations via causal image synthesiscited by 11
  6. 2023Anatomy-Driven Pathology Detection on Chest X-rayscited by 10
  7. 2024Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETcited by 10
  8. 2024Anatomy-guided Pathology Segmentationcited by 7
  9. 2024DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanationscited by 6
  10. 2023Implicit Counterfactual Data Augmentation for Robust Learningcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 6.

10 papers.

  1. 2025Counterfactual Identifiability via Dynamic Optimal Transportcited by 5
  2. 2025Flow Stochastic Segmentation Networkscited by 3
  3. 2025CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray imagescited by 2
  4. 2026Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformerscited by 2
  5. 2026Pixel-level Counterfactual Contrastive Learning for Medical Image Segmentationcited by 1
  6. 2026Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Modelscited by 1
  7. 2025Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CTcited by 0
  8. 2026Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesiscited by 0
  9. 2026Counterfactual Stress Testing for Image Classification Modelscited by 0
  10. 2026TRACE-Seg3D: Counterfactual Context Auditing For Robust 3D Glioma Segmentation Under Institutional Shiftcited by 0