Causal Spatio-Temporal Neural Distance Fields for Counterfactual Cardiac Anatomy
Counterfactual cardiac anatomy can support risk modelling and intervention planning, by predicting how a patient's heart would change under altered clinical conditions. However, current shape generative models are conditional on clinical variables and disregard causal links between these variables. Thus, they cannot answer counterfactual questions about how a given subject's anatomy would change under a different clinical profile. Modelling the heart's continuous movement over the cardiac cycle requires a representation that is itself continuous in space and time. Spatio-temporal neural distance fields provide this while separating clinical conditioning from subject-specific shape. We pla...
| Authors | Mathias Lowes, Kurt Butler, Kristine Aavild Sørensen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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