Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI

• URL: http://arxiv.org/abs/2112.06554v1
• Date: Mon, 13 Dec 2021 10:51:20 GMT
• Title: Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI
• Authors: Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich and Oliver Burgert
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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's challenge, BraTS 2021 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 on the BraTS 2021 validation set, individually. 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.

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