Anatomical Conditioning for Contrastive Unpaired Image-to-Image Translation of Optical Coherence Tomography Images
This work addresses data harmonization for medical imaging across different OCT devices, enabling better disease monitoring, but it is incremental as it builds on an existing contrastive learning approach.
The paper tackles the problem of unpaired image-to-image translation for medical data harmonization between Spectralis-OCT and Home-OCT images, improving semantic consistency and increasing similarity to the target distribution, with results showing enhanced unsupervised segmentation of biomarkers in Home-OCT images.
For a unified analysis of medical images from different modalities, data harmonization using image-to-image (I2I) translation is desired. We study this problem employing an optical coherence tomography (OCT) data set of Spectralis-OCT and Home-OCT images. I2I translation is challenging because the images are unpaired, and a bijective mapping does not exist due to the information discrepancy between both domains. This problem has been addressed by the Contrastive Learning for Unpaired I2I Translation (CUT) approach, but it reduces semantic consistency. To restore the semantic consistency, we support the style decoder using an additional segmentation decoder. Our approach increases the similarity between the style-translated images and the target distribution. Importantly, we improve the segmentation of biomarkers in Home-OCT images in an unsupervised domain adaptation scenario. Our data harmonization approach provides potential for the monitoring of diseases, e.g., age related macular disease, using different OCT devices.