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

翻译：鉴于COVID-19全球大流行，医学图像的计算机辅助诊断备受关注，对计算机断层扫描（CT）图像的鲁棒语义分割方法需求日益迫切。本研究深入分析了数据增强技术如何提升该问题的分割性能。我们在五个公开数据集上评估了20种传统增强技术，测试了六种不同的增强技术应用概率（即对图像施加增强的几率）。同时，我们还评估了一种将训练子集合并为单一更大集合的差异化训练方法。所有网络均通过五折交叉验证策略进行评估，总计开展了超过4600次实验。此外，我们提出了一种基于生成对抗网络（GANs）的新型数据增强技术，用于生成健康和病变的肺部CT图像，并采用与前述传统方法相同的六种概率，评估了该方法的四种变体。研究结果表明，基于GAN的技术和空间级变换最有望提升该问题中深度模型的学习效果，其中StarGANv2 + F在概率为0.3时，在统一训练策略下于Ricord1a数据集上取得了最高的F值。我们的代码已在https://github.com/VRI-UFPR/DACov2022公开。