  Analyzing medical data to find abnormalities is a time-consuming and costly
task, particularly for rare abnormalities, requiring tremendous efforts from
medical experts. Artificial intelligence has become a popular tool for the
automatic processing of medical data, acting as a supportive tool for doctors.
However, the machine learning models used to build these tools are highly
dependent on the data used to train them. Large amounts of data can be
difficult to obtain in medicine due to privacy, expensive and time-consuming
annotations, and a general lack of data samples for infrequent lesions. Here,
we present a novel synthetic data generation pipeline, called SinGAN-Seg, to
produce synthetic medical images with corresponding masks using a single
training image. Our method is different from the traditional GANs because our
model needs only a single image and the corresponding ground truth to train.
Our method produces alternative artificial segmentation datasets with ground
truth masks when real datasets are not allowed to share. The pipeline is
evaluated using qualitative and quantitative comparisons between real and
synthetic data to show that the style transfer technique used in our pipeline
significantly improves the quality of the generated data and our method is
better than other state-of-the-art GANs to prepare synthetic images when the
size of training datasets are limited. By training UNet++ using both real and
the synthetic data generated from the SinGAN-Seg pipeline, we show that models
trained with synthetic data have very close performances to those trained on
real data when the datasets have a considerable amount of data. In contrast,
Synthetic data generated from the SinGAN-Seg pipeline can improve the
performance of segmentation models when training datasets do not have a
considerable amount of data. The code is available on GitHub.
