  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.
