  Effective, robust, and automatic tools for brain tumor segmentation are
needed for the extraction of information useful in treatment planning from
magnetic resonance (MR) images. Context-aware artificial intelligence is an
emerging concept for the development of deep learning applications for
computer-aided medical image analysis. In this work, it is investigated whether
the addition of contextual information from the brain anatomy in the form of
white matter, gray matter, and cerebrospinal fluid masks and probability maps
improves U-Net-based brain tumor segmentation. The BraTS2020 dataset was used
to train and test two standard 3D U-Net models that, in addition to the
conventional MR image modalities, used the anatomical contextual information as
extra channels in the form of binary masks (CIM) or probability maps (CIP). A
baseline model (BLM) that only used the conventional MR image modalities was
also trained. The impact of adding contextual information was investigated in
terms of overall segmentation accuracy, model training time, domain
generalization, and compensation for fewer MR modalities available for each
subject. Results show that there is no statistically significant difference
when comparing Dice scores between the baseline model and the contextual
information models, even when comparing performances for high- and low-grade
tumors independently. Only in the case of compensation for fewer MR modalities
available for each subject did the addition of anatomical contextual
information significantly improve the segmentation of the whole tumor. Overall,
there is no overall significant improvement in segmentation performance when
using anatomical contextual information in the form of either binary masks or
probability maps as extra channels.
