Ensemble CNN Networks for GBM Tumors Segmentation Using Multi-parametric MRI
Zeineldin RA, Karar ME, Mathis-Ullrich F, Burgert O (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12962 LNCS
Pages Range: 473-483
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783031089985
DOI: 10.1007/978-3-031-08999-2_41
Abstract
Glioblastomas are the most aggressive fast-growing primary brain cancer which originate in the glial cells of the brain. Accurate identification of the malignant brain tumor and its sub-regions is still one of the most challenging problems in medical image segmentation. The Brain Tumor Segmentation Challenge (BraTS) has been a popular benchmark for automatic brain glioblastomas segmentation algorithms since its initiation. In this year, BraTS 2021 challenge provides the largest multi-parametric (mpMRI) dataset of 2,000 pre-operative patients. In this paper, we propose a new aggregation of two deep learning frameworks namely, 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. These experimental findings provide evidence that it can be readily applied clinically and thereby aiding in the brain cancer prognosis, therapy planning, and therapy response monitoring. A docker image for reproducing our segmentation results is available online at (https://hub.docker.com/r/razeineldin/deepseg21 ).
Authors with CRIS profile
Involved external institutions
Hochschule Reutlingen
Germany (DE)
Menoufia University (Minoufiya / Minufiya University) / جامعة المنوفية
Egypt (EG)
Karlsruhe Institute of Technology (KIT)
Germany (DE)
Germany (DE)
Menoufia University (Minoufiya / Minufiya University) / جامعة المنوفية
Egypt (EG)
Karlsruhe Institute of Technology (KIT)
Germany (DE)
How to cite
APA:
Zeineldin, R.A., Karar, M.E., Mathis-Ullrich, F., & Burgert, O. (2022). Ensemble CNN Networks for GBM Tumors Segmentation Using Multi-parametric MRI. In Alessandro Crimi, Spyridon Bakas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 473-483). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Zeineldin, Ramy A., et al. "Ensemble CNN Networks for GBM Tumors Segmentation Using Multi-parametric MRI." Proceedings of the 7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Alessandro Crimi, Spyridon Bakas, Springer Science and Business Media Deutschland GmbH, 2022. 473-483.
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