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Synthetic Image Generation of Aortic Valves Using Conditional DDPM

Schnelle Fakten

  • Weitere Publizierende

    Matthis Hofmann

  • Veröffentlichung

    • 2025
    • Band Proceedings of the 11th International Conference on Bioinformatics Research and Applications
  • Titel des Konferenzbands

    Proceedings of the 11th International Conference on Bioinformatics Research and Applications

  • Organisationseinheit

  • Fachgebiete

    • Biomedizinische Technik
    • Ingenieurinformatik/Technische Informatik
    • Medizintechnik
  • Forschungsschwerpunkte

    • BioMedizinTechnik (BMT)
  • Format

    Konferenzpaper

Zitat

M. Hofmann, D. Fromme, T. Streckert, and J. Thiem, “Synthetic Image Generation of Aortic Valves Using Conditional DDPM,” in Proceedings of the 11th International Conference on Bioinformatics Research and Applications, 2025, pp. 9–16.

Abstract

A conditional Denoising Diffusion Probabilistic Model (cDDPM) is trained to generate realistic images of aortic valves that could be used as an advanced data augmentation technique. RGB images of porcine aortic valves and three conditional masks (cusp segmentation, visible and occluded landmarks) serve as training data for the model. The dataset comprises seven porcine hearts and contains 414 training images and 37 test images. Given Gaussian noise image as input, the model is able to generate RGB images of aortic valves that align with the given conditional masks. To enhance realism, the RePaint algorithm is integrated into the image synthesis process, enabling the generation of primary image components alongside small segments from the original image. The synthetic images are evaluated against the test dataset using three common metrics: the Multi-Scale Structural Similarity Index (MS-SSIM) reaches values of up to 0.29, the Kernel Inception Distance (KID) values up to 0.041 and the Fr’echet Inception Distance (FID) values up to 113.5. The generated images closely align with the specified conditional segmentation masks. From a human observer’s perspective, the alignment of the conditional landmarks appears to be less precise compared to the segmentation mask. Conditional masks allow explicit specification of cusp number and geometry, enabling generation of rare aortic valves, including unicuspid and bicuspid types, even though these are not in the training data. cDDPM has demonstrated the capacity to generate realistic aortic valve images. However, the limited data availability and model restrictions resulting from the design process represent limitations and constraints to the model.

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