  The segmentation of coronary arteries by convolutional neural network is
promising yet requires a large amount of labor-intensive manual annotations.
Transferring knowledge from retinal vessels in widely-available public labeled
fundus images (FIs) has a potential to reduce the annotation requirement for
coronary artery segmentation in X-ray angiograms (XAs) due to their common
tubular structures. However, it is challenged by the cross-anatomy domain shift
due to the intrinsically different vesselness characteristics in different
anatomical regions under even different imaging protocols. To solve this
problem, we propose a Semi-Supervised Cross-Anatomy Domain Adaptation (SS-CADA)
which requires only limited annotations for coronary arteries in XAs. With the
supervision from a small number of labeled XAs and publicly available labeled
FIs, we propose a vesselness-specific batch normalization (VSBN) to
individually normalize feature maps for them considering their different
cross-anatomic vesselness characteristics. In addition, to further facilitate
the annotation efficiency, we employ a self-ensembling mean-teacher (SEMT) to
exploit abundant unlabeled XAs by imposing a prediction consistency constraint.
Extensive experiments show that our SS-CADA is able to solve the challenging
cross-anatomy domain shift, achieving accurate segmentation for coronary
arteries given only a small number of labeled XAs.
