  We tackle biomedical image segmentation in the scenario of only a few labeled
brain MR images. This is an important and challenging task in medical
applications, where manual annotations are time-consuming. Current multi-atlas
based segmentation methods use image registration to warp segments from labeled
images onto a new scan. In a different paradigm, supervised learning-based
segmentation strategies have gained popularity. These method consistently use
relatively large sets of labeled training data, and their behavior in the
regime of a few labeled biomedical images has not been thoroughly evaluated. In
this work, we provide two important results for segmentation in the scenario
where few labeled images are available. First, we propose a straightforward
implementation of efficient semi-supervised learning-based registration method,
which we showcase in a multi-atlas segmentation framework. Second, through an
extensive empirical study, we evaluate the performance of a supervised
segmentation approach, where the training images are augmented via random
deformations. Surprisingly, we find that in both paradigms, accurate
segmentation is generally possible even in the context of few labeled images.
