  The variations in multi-center data in medical imaging studies have brought
the necessity of domain adaptation. Despite the advancement of machine learning
in automatic segmentation, performance often degrades when algorithms are
applied on new data acquired from different scanners or sequences than the
training data. Manual annotation is costly and time consuming if it has to be
carried out for every new target domain. In this work, we investigate automatic
selection of suitable subjects to be annotated for supervised domain adaptation
using the concept of reverse classification accuracy (RCA). RCA predicts the
performance of a trained model on data from the new domain and different
strategies of selecting subjects to be included in the adaptation via transfer
learning are evaluated. We perform experiments on a two-center MR database for
the task of organ segmentation. We show that subject selection via RCA can
reduce the burden of annotation of new data for the target domain.
