High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey

Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah

Abstract:

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.