  Various deep learning models have been developed to segment anatomical
structures from medical images, but they typically have poor performance when
tested on another target domain with different data distribution. Recently,
unsupervised domain adaptation methods have been proposed to alleviate this
so-called domain shift issue, but most of them are designed for scenarios with
relatively small domain shifts and are likely to fail when encountering a large
domain gap. In this paper, we propose DCDA, a novel cross-modality unsupervised
domain adaptation framework for tasks with large domain shifts, e.g.,
segmenting retinal vessels from OCTA and OCT images. DCDA mainly consists of a
disentangling representation style transfer (DRST) module and a collaborative
consistency learning (CCL) module. DRST decomposes images into content
components and style codes and performs style transfer and image
reconstruction. CCL contains two segmentation models, one for source domain and
the other for target domain. The two models use labeled data (together with the
corresponding transferred images) for supervised learning and perform
collaborative consistency learning on unlabeled data. Each model focuses on the
corresponding single domain and aims to yield an expertized domain-specific
segmentation model. Through extensive experiments on retinal vessel
segmentation, our framework achieves Dice scores close to target-trained oracle
both from OCTA to OCT and from OCT to OCTA, significantly outperforming other
state-of-the-art methods.
