  Medical image analysis using deep neural networks has been actively studied.
Deep neural networks are trained by learning data. For accurate training of
deep neural networks, the learning data should be sufficient, of good quality,
and should have a generalized property. However, in medical images, it is
difficult to acquire sufficient patient data because of the difficulty of
patient recruitment, the burden of annotation of lesions by experts, and the
invasion of patients' privacy. In comparison, the medical images of healthy
volunteers can be easily acquired. Using healthy brain images, the proposed
method synthesizes multi-contrast magnetic resonance images of brain tumors.
Because tumors have complex features, the proposed method simplifies them into
concentric circles that are easily controllable. Then it converts the
concentric circles into various realistic shapes of tumors through deep neural
networks. Because numerous healthy brain images are easily available, our
method can synthesize a huge number of the brain tumor images with various
concentric circles. We performed qualitative and quantitative analysis to
assess the usefulness of augmented data from the proposed method. Intuitive and
interesting experimental results are available online at
https://github.com/KSH0660/BrainTumor

Image and Video Processing;Computer Vision and Pattern Recognition