  In Computed Tomography, machine learning is often used for automated data
processing. However, increasing model complexity is accompanied by increasingly
large volume datasets, which in turn increases the cost of model training.
Unlike most work that mitigates this by advancing model architectures and
training algorithms, we consider the annotation procedure and its effect on the
model performance. We assume three main virtues of a good dataset collected for
a model training to be label quality, diversity, and completeness. We compare
the effects of those virtues on the model performance using open medical CT
datasets and conclude, that quality is more important than diversity early
during labeling; the diversity, in turn, is more important than completeness.
Based on this conclusion and additional experiments, we propose a labeling
procedure for the segmentation of tomographic images to minimize efforts spent
on labeling while maximizing the model performance.
