  Machine learning and computer vision techniques have grown rapidly in recent
years due to their automation, suitability, and ability to generate astounding
results. Hence, in this paper, we survey the key studies that are published
between 2014 and 2022, showcasing the different machine learning algorithms
researchers have used to segment the liver, hepatic tumors, and
hepatic-vasculature structures. We divide the surveyed studies based on the
tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels),
highlighting the studies that tackle more than one task simultaneously.
Additionally, the machine learning algorithms are classified as either
supervised or unsupervised, and they are further partitioned if the amount of
work that falls under a certain scheme is significant. Moreover, different
datasets and challenges found in literature and websites containing masks of
the aforementioned tissues are thoroughly discussed, highlighting the
organizers' original contributions and those of other researchers. Also, the
metrics used excessively in literature are mentioned in our review, stressing
their relevance to the task at hand. Finally, critical challenges and future
directions are emphasized for innovative researchers to tackle, exposing gaps
that need addressing, such as the scarcity of many studies on the vessels'
segmentation challenge and why their absence needs to be dealt with sooner than
later.
