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

• URL: http://arxiv.org/abs/2603.21213v1
• Date: Sun, 22 Mar 2026 13:12:16 GMT
• Title: Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesis
• Abstract summary: We propose Positional Seg-CFT, which subdivides each structure into regional segments and derives independent measurements per region.<n>Experiments on coronary CT angiography show that Pos-Seg-CFT generates realistic, region-specific modifications, providing finer spatial control for modeling disease progression.
• Score: 12.537078681597498
• License: http://creativecommons.org/licenses/by/4.0/
• 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 angiography show that Pos-Seg-CFT generates realistic, region-specific modifications, providing finer spatial control for modeling disease progression.

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