TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Zeineldin, Ramy
A1  - Karar, Mohamed
A1  - Mathis-Ullrich, Franziska
A1  - Burgert, Oliver
T1  - Ensemble CNN networks for GBM tumors segmentation using multi-parametric MRI
T2  - Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries : 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I (Lecture notes in computer science ; 12962)
N2  - 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 frameworksnamely, 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).
KW  - brain
KW  - BraTS
KW  - CNN
KW  - glioblastoma
KW  - MRI
KW  - segmentation
Y1  - 2022
SN  - 978-3-031-08999-2
SB  - 978-3-031-08999-2
U6  - https://doi.org/10.1007/978-3-031-08999-2_41
DO  - https://doi.org/10.1007/978-3-031-08999-2_41
SP  - 473
EP  - 483
S1  - 11
PB  - Springer
CY  - Cham
ER  -