Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation

Abstract

Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography images is key for diagnosis and personalized treatment of eye diseases. While deep learning has been successful at this task, trained supervised models often fail for images that do not resemble labeled examples, e.g. for images acquired using different devices. We hereby propose a novel semi-supervised learning framework for segmentation of volumetric images from new unlabeled domains. We jointly use supervised and contrastive learning, also introducing a contrastive pairing scheme that leverages similarity between nearby slices in 3D. In addition, we propose channel-wise aggregation as an alternative to conventional spatial-pooling aggregation for contrastive feature map projection. We evaluate our methods for domain adaptation from a (labeled) source domain to an (unlabeled) target domain, each containing images acquired with different acquisition devices. In the target domain, our method achieves a Dice coefficient 13.8% higher than SimCLR (a stateof-the-art contrastive framework), and leads to results comparable to an upper bound with supervised training in that domain. In the source domain, our model also improves the results by 5.4% Dice, by successfully leveraging information from many unlabeled images.

Cost-efficient and glaucoma-specifical model by exploiting normal OCT images with knowledge transfer learning

“…Cho et al proposed a stain-style transfer model to learn similarities and dissimilarities features between different stain-style of histopathological images via adversarial learning, thus encouraging the model to capture shared discriminator knowledge from source datasets [ 40 ]. Alvaro et al proposed a transfer learning model to learn similarities among datasets from different devices via a contrast learning model [ 41 ].…”