  Past few years have witnessed the prevalence of deep learning in many
application scenarios, among which is medical image processing. Diagnosis and
treatment of brain tumors requires an accurate and reliable segmentation of
brain tumors as a prerequisite. However, such work conventionally requires
brain surgeons significant amount of time. Computer vision techniques could
provide surgeons a relief from the tedious marking procedure. In this paper, a
3D U-net based deep learning model has been trained with the help of brain-wise
normalization and patching strategies for the brain tumor segmentation task in
the BraTS 2019 competition. Dice coefficients for enhancing tumor, tumor core,
and the whole tumor are 0.737, 0.807 and 0.894 respectively on the validation
dataset. These three values on the test dataset are 0.778, 0.798 and 0.852.
Furthermore, numerical features including ratio of tumor size to brain size and
the area of tumor surface as well as age of subjects are extracted from
predicted tumor labels and have been used for the overall survival days
prediction task. The accuracy could be 0.448 on the validation dataset, and
0.551 on the final test dataset.
