  Brain tumors are one of the deadliest forms of cancer with a mortality rate
of over 80%. A quick and accurate diagnosis is crucial to increase the chance
of survival. However, in medical analysis, the manual annotation and
segmentation of a brain tumor can be a complicated task. Multiple MRI
modalities are typically analyzed as they provide unique information regarding
the tumor regions. Although these MRI modalities are helpful for segmenting
gliomas, they tend to increase overfitting and computation. This paper proposes
a region of interest detection algorithm that is implemented during data
preprocessing to locate salient features and remove extraneous MRI data. This
decreases the input size, allowing for more aggressive data augmentations and
deeper neural networks. Following the preprocessing of the MRI modalities, a
fully convolutional autoencoder with soft attention segments the different
brain MRIs. When these deep learning algorithms are implemented in practice,
analysts and physicians cannot differentiate between accurate and inaccurate
predictions. Subsequently, test time augmentations and an energy-based model
were used for voxel-based uncertainty predictions. Experimentation was
conducted on the BraTS benchmarks and achieved state-of-the-art segmentation
performance. Additionally, qualitative results were used to assess the
segmentation models and uncertainty predictions.
