[2603.21213] Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesis

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.21213 (cs) [Submitted on 22 Mar 2026] Title:Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesis Authors:Tian Xia, Matthew Sinclair, Andreas Schuh, Fabio De Sousa Ribeiro, Raghav Mehta, Rajat Rasal, Esther Puyol-Antón, Samuel Gerber, Kersten Petersen, Michiel Schaap, Ben Glocker View a PDF of the paper titled Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesis, by Tian Xia and 10 other authors View PDF HTML (experimental) Abstract:Counterfactual image generation enables controlled data augmentation, bias mitigation, and disease modeling. However, existing methods guided by external classifiers or regressors are limited to subject-level factors (e.g., age) and fail to produce localized structural changes, often resulting in global artifacts. Pixel-level guidance using segmentation masks has been explored, but requires user-defined counterfactual masks, which are tedious and impractical. Segmentor-guided Counterfactual Fine-Tuning (Seg-CFT) addressed this by using segmentation-derived measurements to supervise structure-specific variables, yet it remains restricted to global interventions. We propose Positional Seg-CFT, which subdivides each structure into regional segments and derives independent measurements per region, enabling spatially localized and anatomically coherent counterfactuals. Experiments on coronary CT ang...