  We propose a new method that employs transfer learning techniques to
effectively correct sampling selection errors introduced by sparse annotations
during supervised learning for automated tumor segmentation. The practicality
of current learning-based automated tissue classification approaches is
severely impeded by their dependency on manually segmented training databases
that need to be recreated for each scenario of application, site, or
acquisition setup. The comprehensive annotation of reference datasets can be
highly labor-intensive, complex, and error-prone. The proposed method derives
high-quality classifiers for the different tissue classes from sparse and
unambiguous annotations and employs domain adaptation techniques for
effectively correcting sampling selection errors introduced by the sparse
sampling. The new approach is validated on labeled, multi-modal MR images of 19
patients with malignant gliomas and by comparative analysis on the BraTS 2013
challenge data sets. Compared to training on fully labeled data, we reduced the
time for labeling and training by a factor greater than 70 and 180 respectively
without sacrificing accuracy. This dramatically eases the establishment and
constant extension of large annotated databases in various scenarios and
imaging setups and thus represents an important step towards practical
applicability of learning-based approaches in tissue classification.
