  Pancreas segmentation has been traditionally challenging due to its small
size in computed tomography abdominal volumes, high variability of shape and
positions among patients, and blurred boundaries due to low contrast between
the pancreas and surrounding organs. Many deep learning models for pancreas
segmentation have been proposed in the past few years. We present a thorough
systematic review based on the Preferred Reporting Items for Systematic Reviews
and Meta-analyses (PRISMA) statement. The literature search was conducted on
PubMed, Web of Science, Scopus, and IEEE Xplore on original studies published
in peer-reviewed journals from 2013 to 2023. Overall, 130 studies were
retrieved. We initially provided an overview of the technical background of the
most common network architectures and publicly available datasets. Then, the
analysis of the studies combining visual presentation in tabular form and text
description was reported. The tables grouped the studies specifying the
application, dataset size, design (model architecture, learning strategy, and
loss function), results, and main contributions. We first analyzed the studies
focusing on parenchyma segmentation using coarse-to-fine approaches,
multi-organ segmentation, semi-supervised learning, and unsupervised learning,
followed by those studies on generalization to other datasets and those
concerning the design of new loss functions. Then, we analyzed the studies on
segmentation of tumors, cysts, and inflammation reporting multi-stage methods,
semi-supervised learning, generalization to other datasets, and design of new
loss functions. Finally, we provided a critical discussion on the subject based
on the published evidence underlining current issues that need to be addressed
before clinical translation.
