  Due to the COVID-19 global pandemic, computer-assisted diagnoses of medical
images have gained much attention, and robust methods of semantic segmentation
of Computed Tomography (CT) images have become highly desirable. In this work,
we present a deeper analysis of how data augmentation techniques improve
segmentation performance on this problem. We evaluate 20 traditional
augmentation techniques on five public datasets. Six different probabilities of
applying each augmentation technique on an image were evaluated. We also assess
a different training methodology where the training subsets are combined into a
single larger set. All networks were evaluated through a 5-fold
cross-validation strategy, resulting in over 4,600 experiments. We also propose
a novel data augmentation technique based on Generative Adversarial Networks
(GANs) to create new healthy and unhealthy lung CT images, evaluating four
variations of our approach with the same six probabilities of the traditional
methods. Our findings show that GAN-based techniques and spatial-level
transformations are the most promising for improving the learning of deep
models on this problem, with the StarGANv2 + F with a probability of 0.3
achieving the highest F-score value on the Ricord1a dataset in the unified
training strategy. Our code is publicly available at
https://github.com/VRI-UFPR/DACov2022
