  Today, deep convolutional neural networks (CNNs) have demonstrated state of
the art performance for supervised medical image segmentation, across various
imaging modalities and tasks. Despite early success, segmentation networks may
still generate anatomically aberrant segmentations, with holes or inaccuracies
near the object boundaries. To mitigate this effect, recent research works have
focused on incorporating spatial information or prior knowledge to enforce
anatomically plausible segmentation. If the integration of prior knowledge in
image segmentation is not a new topic in classical optimization approaches, it
is today an increasing trend in CNN based image segmentation, as shown by the
growing literature on the topic. In this survey, we focus on high level prior,
embedded at the loss function level. We categorize the articles according to
the nature of the prior: the object shape, size, topology, and the
inter-regions constraints. We highlight strengths and limitations of current
approaches, discuss the challenge related to the design and the integration of
prior-based losses, and the optimization strategies, and draw future research
directions.
