  Pre-processing and Data Augmentation play an important role in Deep
Convolutional Neural Networks (DCNN). Whereby several methods aim for
standardization and augmentation of the dataset, we here propose a novel method
aimed to feed DCNN with spherical space transformed input data that could
better facilitate feature learning compared to standard Cartesian space images
and volumes. In this work, the spherical coordinates transformation has been
applied as a preprocessing method that, used in conjunction with normal MRI
volumes, improves the accuracy of brain tumor segmentation and patient overall
survival (OS) prediction on Brain Tumor Segmentation (BraTS) Challenge 2020
dataset. The LesionEncoder framework has been then applied to automatically
extract features from DCNN models, achieving 0.586 accuracy of OS prediction on
the validation data set, which is one of the best results according to BraTS
2020 leaderboard.
