  The success of supervised lesion segmentation algorithms using Computed
Tomography (CT) exams depends significantly on the quantity and variability of
samples available for training. While annotating such data constitutes a
challenge itself, the variability of lesions in the dataset also depends on the
prevalence of different types of lesions. This phenomenon adds an inherent bias
to lesion segmentation algorithms that can be diminished, among different
possibilities, using aggressive data augmentation methods. In this paper, we
present a method for implanting realistic lesions in CT slices to provide a
rich and controllable set of training samples and ultimately improving semantic
segmentation network performances for delineating lesions in CT exams. Our
results show that implanting synthetic lesions not only improves (up to around
12\%) the segmentation performance considering different architectures but also
that this improvement is consistent among different image synthesis networks.
We conclude that increasing the variability of lesions synthetically in terms
of size, density, shape, and position seems to improve the performance of
segmentation models for liver lesion segmentation in CT slices.
