A quasi-conformal mapping-based data augmentation technique for brain tumor segmentation

Deep neural networks (DNNs) have been widely used in the medical imaging field. The large and high quality dataset is crucial for the performance of the deep learning models, but the medical data and ground-truth is often insufficient and very expensive in terms of time and human effort on the data collection. However, we can improve the performance of the deep learning model by augmenting the data we already have. In this work, we introduce a novel differential geometry-based quasi conformal (QC) mapping augmentation technique to augment the brain tumor images. The QC method lets the user specify or randomly generate a complex-valued function on the image domain via Beltrami coefficient. By solving the Beltrami equation with given Beltrami coefficient, the QC map, which can further guide the deformation of the image, is able to generate all possible linear and non-linear image warpings and it is flexible to allow the user to fully control the global and local deformations. Our experimental results demonstrate the efficiency and efficacy of the proposed method.