  Unsupervised domain adaptation has been proposed recently to tackle the
so-called domain shift between training data and test data with different
distributions. However, most of them only focus on single-target domain
adaptation and cannot be applied to the scenario with multiple target domains.
In this paper, we propose RVms, a novel unsupervised multi-target domain
adaptation approach to segment retinal vessels (RVs) from multimodal and
multicenter retinal images. RVms mainly consists of a style augmentation and
transfer (SAT) module and a dual-teacher knowledge distillation (DTKD) module.
SAT augments and clusters images into source-similar domains and
source-dissimilar domains via Bezier and Fourier transformations. DTKD utilizes
the augmented and transformed data to train two teachers, one for
source-similar domains and the other for source-dissimilar domains. Afterwards,
knowledge distillation is performed to iteratively distill different domain
knowledge from teachers to a generic student. The local relative intensity
transformation is employed to characterize RVs in a domain invariant manner and
promote the generalizability of teachers and student models. Moreover, we
construct a new multimodal and multicenter vascular segmentation dataset from
existing publicly-available datasets, which can be used to benchmark various
domain adaptation and domain generalization methods. Through extensive
experiments, RVms is found to be very close to the target-trained Oracle in
terms of segmenting the RVs, largely outperforming other state-of-the-art
methods.
