  Magnetic resonance imaging (MRI) data is heterogeneous due to differences in
device manufacturers, scanning protocols, and inter-subject variability. A
conventional way to mitigate MR image heterogeneity is to apply preprocessing
transformations such as anatomy alignment, voxel resampling, signal intensity
equalization, image denoising, and localization of regions of interest.
Although a preprocessing pipeline standardizes image appearance, its influence
on the quality of image segmentation and on other downstream tasks in deep
neural networks has never been rigorously studied.
  We conduct experiments on three publicly available datasets and evaluate the
effect of different preprocessing steps in intra- and inter-dataset training
scenarios. Our results demonstrate that most popular standardization steps add
no value to the network performance; moreover, preprocessing can hamper model
performance. We suggest that image intensity normalization approaches do not
contribute to model accuracy because of the reduction of signal variance with
image standardization. Finally, we show that the contribution of
skull-stripping in data preprocessing is almost negligible if measured in terms
of estimated tumor volume.
  We show that the only essential transformation for accurate deep learning
analysis is the unification of voxel spacing across the dataset. In contrast,
inter-subjects anatomy alignment in the form of non-rigid atlas registration is
not necessary and intensity equalization steps (denoising, bias-field
correction and histogram matching) do not improve models' performance. The
study code is accessible online
https://github.com/MedImAIR/brain-mri-processing-pipeline
