  Today, deep convolutional neural networks (CNNs) have demonstrated
state-of-the-art performance for medical image segmentation, on 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 enforce anatomical plausibility, recent research
studies have focused on incorporating prior knowledge such as object shape or
boundary, as constraints in the loss function. Prior integrated could be
low-level referring to reformulated representations extracted from the
ground-truth segmentations, or high-level representing external medical
information such as the organ's shape or size. Over the past few years,
prior-based losses exhibited a rising interest in the research field since they
allow integration of expert knowledge while still being architecture-agnostic.
However, given the diversity of prior-based losses on different medical imaging
challenges and tasks, it has become hard to identify what loss works best for
which dataset. In this paper, we establish a benchmark of recent prior-based
losses for medical image segmentation. The main objective is to provide
intuition onto which losses to choose given a particular task or dataset. To
this end, four low-level and high-level prior-based losses are selected. The
considered losses are validated on 8 different datasets from a variety of
medical image segmentation challenges including the Decathlon, the ISLES and
the WMH challenge. Results show that whereas low-level prior-based losses can
guarantee an increase in performance over the Dice loss baseline regardless of
the dataset characteristics, high-level prior-based losses can increase
anatomical plausibility as per data characteristics.
