  Segmentation of anatomical structures is a fundamental image analysis task
for many applications in the medical field. Deep learning methods have been
shown to perform well, but for this purpose large numbers of manual annotations
are needed in the first place, which necessitate prohibitive levels of
resources that are often unavailable. In an active learning framework of
selecting informed samples for manual labeling, expert clinician time for
manual annotation can be optimally utilized, enabling the establishment of
large labeled datasets for machine learning. In this paper, we propose a novel
method that combines representativeness with uncertainty in order to estimate
ideal samples to be annotated, iteratively from a given dataset. Our novel
representativeness metric is based on Bayesian sampling, by using
information-maximizing autoencoders. We conduct experiments on a shoulder
magnetic resonance imaging (MRI) dataset for the segmentation of four
musculoskeletal tissue classes. Quantitative results show that the annotation
of representative samples selected by our proposed querying method yields an
improved segmentation performance at each active learning iteration, compared
to a baseline method that also employs uncertainty and representativeness
metrics. For instance, with only 10% of the dataset annotated, our method
reaches within 5% of Dice score expected from the upper bound scenario of all
the dataset given as annotated (an impractical scenario due to resource
constraints), and this gap drops down to a mere 2% when less than a fifth of
the dataset samples are annotated. Such active learning approach to selecting
samples to annotate enables an optimal use of the expert clinician time, being
often the bottleneck in realizing machine learning solutions in medicine.
