  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 state-of-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.
