Content-Preserving Diffusion Model for Unsupervised AS-OCT Image Despeckling

Sanqian Li, Risa Higashita, Huazhu Fu, Heng Li, Jingxuan Niu, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


Anterior segment optical coherence tomography (AS-OCT) is a non-invasive imaging technique that is highly valuable for ophthalmic diagnosis. However, speckles in AS-OCT images can often degrade the image quality and affect clinical analysis. As a result, removing speckles in AS-OCT images can greatly benefit automatic ophthalmology analysis. Unfortunately, challenges still exist in deploying effective AS-OCT image denoising algorithms, including collecting sufficient paired training data and the requirement to preserve consistent content in medical images. To address these practical issues, we propose an unsupervised AS-OCT despeckling algorithm via Content Preserving Diffusion Model (CPDM) with statistical knowledge. At the training stage, a Markov chain transforms clean images to white Gaussian noise by repeatedly adding random noise and removes the predicted noise in a reverse procedure. At the inference stage, we first analyze the statistical distribution of speckles and convert it into a Gaussian distribution, aiming to match the fast truncated reverse diffusion process. We then explore the posterior distribution of observed images as a fidelity term to ensure content consistency in the iterative procedure. Our experimental results show that CPDM significantly improves image quality compared to competitive methods. Furthermore, we validate the benefits of CPDM for subsequent clinical analysis, including ciliary muscle (CM) segmentation and scleral spur (SS) localization.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
EditorsHayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031439896
Publication statusPublished - 2023
Externally publishedYes
Event26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada
Duration: 8 Oct 202312 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14226 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023


  • Diffusion model
  • Unsupervised despeckling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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