  Magnetic Resonance Imaging (MRI) is used in everyday clinical practice to
assess brain tumors. Several automatic or semi-automatic segmentation
algorithms have been introduced to segment brain tumors and achieve an
expert-like accuracy. Deep Convolutional Neural Networks (DCNN) have recently
shown very promising results, however, DCNN models are still far from achieving
clinically meaningful results mainly because of the lack of generalization of
the models. DCNN models need large annotated datasets to achieve good
performance. Models are often optimized on the domain dataset on which they
have been trained, and then fail the task when the same model is applied to
different datasets from different institutions. One of the reasons is due to
the lack of data standardization to adjust for different models and MR
machines. In this work, a 3D Spherical coordinates transform during the
pre-processing phase has been hypothesized to improve DCNN models' accuracy and
to allow more generalizable results even when the model is trained on small and
heterogeneous datasets and translated into different domains. Indeed, the
spherical coordinate system avoids several standardization issues since it
works independently of resolution and imaging settings. Both Cartesian and
spherical volumes were evaluated in two DCNN models with the same network
structure using the BraTS 2019 dataset. The model trained on spherical
transform pre-processed inputs resulted in superior performance over the
Cartesian-input trained model on predicting gliomas' segmentation on tumor core
and enhancing tumor classes (increase of 0.011 and 0.014 respectively on the
validation dataset), achieving a further improvement in accuracy by merging the
two models together. Furthermore, the spherical transform is not
resolution-dependent and achieve same results on different input resolution.
