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ARXIV:2602.11942 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.11942MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A framework that uses implicit neural representations and diffusion models to generate synthetic LGE images for improved cardiac scar segmentation.
Opportunity summary
Pain A framework that uses implicit neural representations and diffusion models to generate synthetic LGE images for improved cardiac scar segmentation.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A framework that uses implicit neural representations and diffusion models to generate synthetic LGE images for improved cardiac scar segmentation. We propose a novel framework that synthesises both LGE images and their corresponding segmentation…
Late gadolinium enhancement (LGE) imaging is the clinical standard for myocardial scar assessment, but limited annotated datasets hinder the development of automated segmentation methods. We propose a novel framework that synthesises both LGE images…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our approach provides an annotation-free method to help mitigate data scarcity.The code for this research is publicly available.
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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A framework that uses implicit neural representations and diffusion models to generate synthetic LGE images for improved cardiac scar segmentation.
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10.48550/arXiv.2602.11942A framework that uses implicit neural representations and diffusion models to generate synthetic LGE images for improved cardiac scar segmentation.
Abstract
Late gadolinium enhancement (LGE) imaging is the clinical standard for myocardial scar assessment, but limited annotated datasets hinder the development of automated segmentation methods. We propose a novel framework that synthesises both LGE images and their corresponding segmentation masks using implicit neural representations (INRs) combined with denoising diffusion models. Our approach first trains INRs to capture continuous spatial representations of LGE data and associated myocardium and fibrosis masks. These INRs are then compressed into compact latent embeddings, preserving essential anatomical information. A diffusion model operates on this latent space to generate new representations, which are decoded into synthetic LGE images with anatomically consistent segmentation masks. Experiments on 133 cardiac MRI scans suggest that augmenting training data with 200 synthetic volumes contributes to improved fibrosis segmentation performance, with the Dice score showing an increase from 0.509 to 0.524. Our approach provides an annotation-free method to help mitigate data scarcity.The code for this research is publicly available.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 7.0
PROBLEM
A framework that uses implicit neural representations and diffusion models to generate synthetic LGE images for improved cardiac scar segmentation. We propose a novel framework that synthesises both LGE images and their corresponding segmentation masks using implicit neural repr...
METHOD
Late gadolinium enhancement (LGE) imaging is the clinical standard for myocardial scar assessment, but limited annotated datasets hinder the development of automated segmentation methods. We propose a novel framework that synthesises both LGE images and their corresponding segme...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our approach provides an annotation-free method to help mitigate data scarcity.The code for this research is publicly available.
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A framework that uses implicit neural representations and diffusion models to generate synthetic LGE images for improved cardiac scar segmentation. We propose a novel framework that synthesises both LGE images and their corresponding segmentation masks using implicit neural representations (INRs) combined with denoising diffusion models.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Late gadolinium enhancement (LGE) imaging is the clinical standard for myocardial scar assessment, but limited annotated datasets hinder the development of automated segmentation methods. We propose a novel framework that synthesises both LGE images and their corresponding segmentation masks using implicit neural representations (INRs) combined with denoising diffusion models.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our approach provides an annotation-free method to help mitigate data scarcity.The code for this research is publicly available.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework that uses implicit neural representations and diffusion models to generate synthetic LGE images for improved cardiac scar segmentation.
Segment
Medical AI
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Commercial read
7.0/10 public viability
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