Noise transfer for unsupervised domain adaptation of retinal OCT images

TL;DR

This paper introduces SVDNA, a simple noise-based domain adaptation method using SVD to improve retinal OCT image segmentation across different devices, outperforming complex methods.

Contribution

Proposes a minimal noise adaptation technique based on SVD that effectively bridges domain gaps in retinal OCT imaging without altering model architecture.

Findings

SVDNA outperforms state-of-the-art unsupervised domain adaptation methods.

The method is simple to implement and integrates easily into existing pipelines.

SVDNA effectively transfers style between unlabeled target and labeled source images.

Abstract

Optical coherence tomography (OCT) imaging from different camera devices causes challenging domain shifts and can cause a severe drop in accuracy for machine learning models. In this work, we introduce a minimal noise adaptation method based on a singular value decomposition (SVDNA) to overcome the domain gap between target domains from three different device manufacturers in retinal OCT imaging. Our method utilizes the difference in noise structure to successfully bridge the domain gap between different OCT devices and transfer the style from unlabeled target domain images to source images for which manual annotations are available. We demonstrate how this method, despite its simplicity, compares or even outperforms state-of-the-art unsupervised domain adaptation methods for semantic segmentation on a public OCT dataset. SVDNA can be integrated with just a few lines of code into the augmentation pipeline of any network which is in contrast to many state-of-the-art domain adaptation methods which often need to change the underlying model architecture or train a separate style transfer model. The full code implementation for SVDNA is available at https://github.com/ValentinKoch/SVDNA.