Flow-based conditional cardiac anatomy generation for virtual cohorts
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TL;DR - CAN-FLOW is a two-step conditional generative framework using normalizing flows to synthesize biventricular cardiac anatomies conditioned on sex, age, and BMI, aimed at building virtual cohorts for cardiac digital twins and in silico trials when real imaging-derived anatomy data is scarce or restricted.
- Decouples representation learning from conditioning: first learns geometry-only latent representations of diffeomorphic cardiac shape momenta, then fits a conditional normalizing flow over that latent space — unlike cVAEs, which entangle both via a shared regularized latent prior.
- Trained on 2,208 healthy UK Biobank subjects and benchmarked against cVAEs across a range of regularization strengths.
- Reported gains over cVAE baselines on clinical phenotype distributions, metadata-dependent trends, subgroup variability, point-cloud coverage, and high-dimensional shape variability.
- Positioned as a shareable alternative to distributing restricted imaging data, addressing cohort size limits, subgroup sparsity, and data-sharing constraints.
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Flow-based conditional cardiac anatomy generation for virtual cohorts
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TL;DR - CAN-FLOW is a two-step conditional generative framework using normalizing flows to synthesize biventricular cardiac anatomies conditioned on sex, age, and BMI, aimed at building virtual cohorts for cardiac digital twins and in silico trials when real imaging-derived anatomy data is scarce or restricted.
- Decouples representation learning from conditioning: first learns geometry-only latent representations of diffeomorphic cardiac shape momenta, then fits a conditional normalizing flow over that latent space — unlike cVAEs, which entangle both via a shared regularized latent prior.
- Trained on 2,208 healthy UK Biobank subjects and benchmarked against cVAEs across a range of regularization strengths.
- Reported gains over cVAE baselines on clinical phenotype distributions, metadata-dependent trends, subgroup variability, point-cloud coverage, and high-dimensional shape variability.
- Positioned as a shareable alternative to distributing restricted imaging data, addressing cohort size limits, subgroup sparsity, and data-sharing constraints.