  With the COVID-19 global pandemic, computerassisted diagnoses of medical
images have gained a lot of attention, and robust methods of Semantic
Segmentation of Computed Tomography (CT) turned highly desirable. Semantic
Segmentation of CT is one of many research fields of automatic detection of
Covid-19 and was widely explored since the Covid19 outbreak. In the robotic
field, Semantic Segmentation of organs and CTs are widely used in robots
developed for surgery tasks. As new methods and new datasets are proposed
quickly, it becomes apparent the necessity of providing an extensive evaluation
of those methods. To provide a standardized comparison of different
architectures across multiple recently proposed datasets, we propose in this
paper an extensive benchmark of multiple encoders and decoders with a total of
120 architectures evaluated in five datasets, with each dataset being validated
through a five-fold cross-validation strategy, totaling 3.000 experiments. To
the best of our knowledge, this is the largest evaluation in number of
encoders, decoders, and datasets proposed in the field of Covid-19 CT
segmentation.
