BraTS 2022 Winning Glioma Segmentation Solution on the BraTS and SSA test datasets.
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This is a docker image for reproducing our segmentation results of the paper:
Zeineldin, R.A., Karar, M.E., Mathis-Ullrich, F., Burgert, O. (2022). Ensemble CNN Networks for GBM Tumors Segmentation Using Multi-parametric MRI. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12962. Springer, Cham. https://doi.org/10.1007/978-3-031-08999-2_41
In this paper, we proposed a new aggregation of two deep learning frameworks, DeepSeg and nnU-Net, for automatic glioblastoma recognition in pre-operative mpMRI. Our ensemble method obtains Dice similarity scores of 92.00, 87.33, and 84.10 and Hausdorff Distances of 3.81, 8.91, and 16.02 for the enhancing tumor, tumor core, and whole tumor regions, respectively, on the BraTS 2021 validation set, ranking us among the top ten teams.
For more information about the data format, how to run the docker, and the BraTS Challenge, please refer to the following website https://www.synapse.org/#!Synapse:syn27046444/wiki/616571
Content type
Image
Digest
sha256:72ee5f96d…
Size
14.5 GB
Last updated
about 3 years ago
docker pull razeineldin/camed22