  The ability to quickly annotate medical imaging data plays a critical role in
training deep learning frameworks for segmentation. Doing so for image volumes
or video sequences is even more pressing as annotating these is particularly
burdensome. To alleviate this problem, this work proposes a new method to
efficiently segment medical imaging volumes or videos using point-wise
annotations only. This allows annotations to be collected extremely quickly and
remains applicable to numerous segmentation tasks. Our approach trains a deep
learning model using an appropriate Positive/Unlabeled objective function using
sparse point-wise annotations. While most methods of this kind assume that the
proportion of positive samples in the data is known a-priori, we introduce a
novel self-supervised method to estimate this prior efficiently by combining a
Bayesian estimation framework and new stopping criteria. Our method iteratively
estimates appropriate class priors and yields high segmentation quality for a
variety of object types and imaging modalities. In addition, by leveraging a
spatio-temporal tracking framework, we regularize our predictions by leveraging
the complete data volume. We show experimentally that our approach outperforms
state-of-the-art methods tailored to the same problem.
