Retinal OCT Synthesis Using DDPM for Layer Segmentation

Temel Kavramlar

The author proposes using denoising diffusion probabilistic models (DDPMs) to automatically generate realistic retinal optical coherence tomography (OCT) images, improving layer segmentation accuracy through knowledge adaptation.

Özet

The study introduces a method utilizing DDPMs to synthesize retinal OCT images, demonstrating improved segmentation results. By distilling pseudo labels from synthesized images and sketches, the research highlights the potential of DDPMs in reducing manual annotations for OCT datasets.

İstatistikler

"We utilized the training set from the GOALS Challenge in MICCAI 2022, which consists of 100 circumpapillary OCT images with a resolution of 1100×800 pixels."
"The total Dice scores of RNFL, GCIPL and CL are weighted with factors of 0.4, 0.3 and 0.3, respectively."
"A comparison of histograms in the forward diffusion process of a real image and a sketch w.r.t. t is shown in Fig. 2."
"An ablation study of the preprocessing steps on the sketch is conducted and listed in Tab. 1 with tstart = 300 and 5 different networks."
"Models trained on a fully-synthesized dataset (0/1000) can perform on par with the ones using a real dataset (50/0)."

Alıntılar

"We propose an image synthesis method that utilizes denoising diffusion probabilistic models (DDPMs) to automatically generate retinal optical coherence tomography (OCT) images."
"Moreover, our research suggests that a layer segmentation model exclusively trained on synthesized OCT images can achieve comparable results to a model trained exclusively with real images."
"These findings demonstrate the promising potential of DDPMs in reducing the need for manual annotations of retinal OCT images."

Önemli Bilgiler Şuradan Elde Edildi

Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation

by Yuli Wu,Weid... : arxiv.org 03-08-2024

https://arxiv.org/pdf/2311.05479.pdf

Daha Derin Sorular

How can generative variety be balanced with histological structure invariance when synthesizing OCT images

When synthesizing OCT images, balancing generative variety with histological structure invariance is crucial for producing realistic and accurate images. One approach to achieve this balance is through parameter tuning of the Denoising Diffusion Probabilistic Models (DDPMs). By adjusting parameters such as the starting timestep during image generation, optimizing sketch parametrization for layer thickness and intensity, and incorporating preprocessing steps like blurring and perturbation, it is possible to enhance the fidelity of synthesized images while maintaining structural accuracy. Additionally, employing knowledge adaptation techniques, such as teacher-student distillation architectures, can help refine pseudo labels generated from initial sketches to better align with histological structures present in real OCT images. This iterative process of fine-tuning DDPMs based on both generative variety and histological structure feedback can lead to more realistic synthetic OCT images that are suitable for various applications.

What are potential applications beyond layer segmentation where DDPMs could be beneficial

Beyond layer segmentation, Denoising Diffusion Probabilistic Models (DDPMs) hold potential for a wide range of applications in biomedical imaging. One promising application is in unsupervised domain adaptation between different OCT scanners. DDPMs can be utilized to generate synthetic OCT images that mimic variations across scanner types or imaging conditions without requiring manual annotations. By leveraging the generative capabilities of DDPMs along with their ability to capture complex data distributions accurately, researchers can create diverse datasets that facilitate robust model training across heterogeneous data sources. Furthermore, DDPMs could be beneficial in tasks such as anomaly detection in retinal scans or generating augmented datasets for training deep learning models used in disease classification or progression monitoring.

How might unsupervised domain adaptation between different OCT scanners benefit from DDPMs

Unsupervised domain adaptation between different OCT scanners stands to benefit significantly from the capabilities of Denoising Diffusion Probabilistic Models (DDPMs). In this scenario, DDPMs can be employed to synthesize realistic OCT images that bridge the gap between source and target domains represented by distinct scanner characteristics or imaging protocols. By generating synthetic data using DDPMs trained on source domain information but adapted towards target domain features through diffusion processes and noise modeling adjustments, unsupervised domain adaptation becomes feasible without labeled data from the target scanner. This approach enables seamless integration of new scanner technologies into existing workflows without extensive manual annotation efforts while ensuring robust performance across varied imaging setups.