mlsanityData to 14 Oct 2026

Deep Generative Models for Enhanced Vitreous OCT Imaging

arXiv 2511.00881 ↗First posted 2 Nov 2025eess.IV cs.AI cs.LGcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Purpose: To evaluate deep learning (DL) models for enhancing vitreous optical coherence tomography (OCT) image quality and reducing acquisition time. Methods: Conditional Denoising Diffusion Probabilistic Models (cDDPMs), Brownian Bridge Diffusion Models (BBDMs), U-Net, Pix2Pix, and Vector-Quantised Generative Adversarial Network (VQ-GAN) were used to generate high-quality spectral-domain (SD) vitreous OCT images. Inputs were SD ART10 images, and outputs were compared to pseudoART100 images obtained by averaging ten ART10 images per eye location. Model performance was assessed using image quality metrics and Visual Turing Tests, where ophthalmologists ranked generated images and evaluated anatomical fidelity. The best model's performance was further tested within the manually segmented vitreous on newly acquired data. Results: U-Net achieved the highest Peak Signal-to-Noise Ratio (PSNR: 30.230) and Structural Similarity Index Measure (SSIM: 0.820), followed by cDDPM. For Learned Perceptual Image Patch Similarity (LPIPS), Pix2Pix (0.697) and cDDPM (0.753) performed best. In the first Visual Turing Test, cDDPM ranked highest (3.07); in the second (best model only), cDDPM achieved a 32.9% fool rate and 85.7% anatomical preservation. On newly acquired data, cDDPM generated vitreous regions more similar in PSNR to the ART100 reference than true ART1 or ART10 B-scans and achieved higher PSNR on whole images when conditioned on ART1 than ART10. Conclusions: Results reveal discrepancies between quantitative metrics and clinical evaluation, highlighting the need for combined assessment. cDDPM showed strong potential for generating clinically meaningful vitreous OCT images while reducing acquisition time fourfold. Translational Relevance: cDDPMs show promise for clinical integration, supporting faster, higher-quality vitreous imaging. Dataset and code will be made publicly available.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 23 of its 53 references that are in the map. First posted on arXiv 2 Nov 2025.

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23 of 53 references.

Closest ancestors · 7

Same direction, newest first

  1. 2025fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting17 Jul
  2. 2024Conditioning 3D Diffusion Models with 2D Images: Towards Standardized OCT Volumes through En Face-Informed Super-Resolution13 Oct
  3. 2024Denoising Diffusion Models for Inpainting of Healthy Brain Tissue27 Feb
  4. 2023Content-Preserving Diffusion Model for Unsupervised AS-OCT image Despeckling30 Jun
  5. 2023Diffusion Models for Contrast Harmonization of Magnetic Resonance Images14 Mar
  6. 2022Diffusion Models for Medical Anomaly Detection8 Mar
  7. 2021Diffusion Models for Implicit Image Segmentation Ensembles6 Dec

Other references · 16

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2020Denoising Diffusion Implicit Modelscited by 9,781
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  6. 2023Segment Anythingcited by 8,234
  7. 2022Generative Adversarial Networkscited by 5,627
  8. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  9. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  10. 2021Palette: Image-to-Image Diffusion Modelscited by 1,300
  11. 2021Vector-quantized Image Modeling with Improved VQGANcited by 658
  12. 2019MaskGAN: Towards Diverse and Interactive Facial Image Manipulationcited by 628
4 more
  1. 2022BBDM: Image-to-image Translation with Brownian Bridge Diffusion Modelscited by 225
  2. 2017How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosiscited by 35
  3. 2024Deep Generative Models for 3D Medical Image Synthesiscited by 14
  4. 2024EBDM: Exemplar-guided Image Translation with Brownian-bridge Diffusion Modelscited by 8

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