Fully Automatic Binary Glioma Grading based on Pre-Therapy MRI using 3D Convolutional Neural NetworksDownload PDF

12 Apr 2019 (modified: 05 May 2023)MIDL Abstract 2019Readers: Everyone
Keywords: Deep Learning, CNN, Glioma Grading, MRI
TL;DR: Using 3D segmentation and classification CNNs for automatic binary glioma grading.
Abstract: The optimal treatment strategy of newly diagnosed glioma is strongly influenced by tumour malignancy. Manual non-invasive grading based on MRI is not always accurate and biopsies to verify diagnosis negatively impact overall survival. In this paper, we propose a fully automatic 3D computer-aided diagnosis (CAD) system to non-invasively differentiate high-grade glioblastoma from lower-grade glioma. The approach consists of an automatic segmentation step to extract the tumour ROI followed by classification using a 3D convolutional neural network. Segmentation was performed using a 3D U-Net achieving a dice score of 88.53% which matches top performing algorithms in the BraTS 2018 challenge. The classification network was trained and evaluated on a large heterogeneous dataset of 549 patients reaching an accuracy of 91%. Additionally, the CAD system was evaluated on data from the Ghent University Hospital and achieved an accuracy of 92% which shows that the algorithm is robust to data from different centres.
Code Of Conduct: I have read and accept the code of conduct.
Remove If Rejected: Remove submission from public view if paper is rejected.
3 Replies