  In brain tumor diagnosis and surgical planning, segmentation of tumor regions
and accurate analysis of surrounding normal tissues are necessary for
physicians. Pathological variability often renders difficulty to register a
well-labeled normal atlas to such images and to automatic segment/label
surrounding normal brain tissues. In this paper, we propose a new registration
approach that first segments brain tumor using a U-Net and then simulates
missed normal tissues within the tumor region using a partial convolutional
network. Then, a standard normal brain atlas image is registered onto such
tumor-removed images in order to segment/label the normal brain tissues. In
this way, our new approach greatly reduces the effects of pathological
variability in deformable registration and segments the normal tissues
surrounding brain tumor well. In experiments, we used MICCAI BraTS2018 T1 tumor
images to evaluate the proposed algorithm. By comparing direct registration
with the proposed algorithm, the results showed that the Dice coefficient for
gray matters was significantly improved for surrounding normal brain tissues.
