Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automated liver tissues delineation based on machine learning
  techniques: A survey, current trends and future orientations   There is no denying how machine learning and computer vision have grown in
the recent years. Their highest advantages lie within their automation,
suitability, and ability to generate astounding results in a matter of seconds
in a reproducible manner. This is aided by the ubiquitous advancements reached
in the computing capabilities of current graphical processing units and the
highly efficient implementation of such techniques. Hence, in this paper, we
survey the key studies that are published between 2014 and 2020, 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 further partitioned if the
amount of works that fall 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 that are used excessively in literature are mentioned in our review
stressing their relevancy 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 in an
accelerated manner.

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The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin