  Automatic segmentation is essential for the brain tumor diagnosis, disease
prognosis, and follow-up therapy of patients with gliomas. Still, accurate
detection of gliomas and their sub-regions in multimodal MRI is very
challenging due to the variety of scanners and imaging protocols. Over the last
years, the BraTS Challenge has provided a large number of multi-institutional
MRI scans as a benchmark for glioma segmentation algorithms. This paper
describes our contribution to the BraTS 2022 Continuous Evaluation challenge.
We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg,
nnU-Net, and DeepSCAN for automatic glioma boundaries detection in
pre-operative MRI. It is worth noting that our ensemble models took first place
in the final evaluation on the BraTS testing dataset with Dice scores of
0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05,
for the whole tumor, tumor core, and enhancing tumor, respectively.
Furthermore, the proposed ensemble method ranked first in the final ranking on
another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean
Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the
whole tumor, tumor core, and enhancing tumor, respectively. The docker image
for the winning submission is publicly available at
(https://hub.docker.com/r/razeineldin/camed22).
