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

TL;DR

This paper introduces an ensemble deep learning approach combining DeepSeg and nnU-Net for glioblastoma segmentation in multi-parametric MRI, achieving high accuracy on the BraTS 2021 challenge dataset.

Contribution

It presents a novel ensemble framework for brain tumor segmentation that outperforms many existing methods and is ready for clinical application.

Findings

Achieved Dice scores of 92.00, 87.33, and 84.10 for tumor regions.

Ranked among the top ten teams in BraTS 2021.

Demonstrated clinical applicability of the method.

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).