Deep Hourglass for Brain Tumor Segmentation

Benson, Eze; Pound, Michael P.; French, Andrew P.; Jackson, Aaron S.; Pridmore, Tony P.

Abstract

The segmentation of a brain tumour in an MRI scan is a challenging task, in this paper we present our results for this problem via the BraTS 2018 challenge, consisting of 210 high grade glioma (HGG) and 75 low grade glioma (LGG) volumes for training. We train and evaluate a convolutional neural network (CNN) encoder-decoder network based on a singular hourglass structure. The hourglass network is able to classify the whole tumour (WT), enhancing (ET) tumour and core tumour (TC) in one pass. We apply a small amount of preprocessing to the data before feeding it to the network but no post processing. We apply our method to two different unseen sets of volumes containing 66 and 191 volumes. We achieve an overall Dice coefficient of 92% on the training set. On the first unseen set our network achieves Dice coefficients of 0.66, 0.82 and 0.72 for ET, WT and TC. On the second unseen set our network achieves Dice coefficients of 0.62, 0.79 and 0.65 on ET, WT and TC.

Citation

Benson, E., Pound, M. P., French, A. P., Jackson, A. S., & Pridmore, T. P. (2018, September). Deep Hourglass for Brain Tumor Segmentation. Presented at 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain

• Presentation Conference Type: Edited Proceedings
• Conference Name: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018
• Start Date: Sep 16, 2018
• End Date: Sep 16, 2018
• Acceptance Date: Jan 1, 2019
• Online Publication Date: Jan 26, 2019
• Publication Date: 2019
• Deposit Date: May 16, 2019
• Publicly Available Date: May 16, 2019
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 419-428
• Series Title: Lecture Notes in Computer Science
• Series Number: 11384
• Series ISSN: 1611-3349
• Book Title: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuriesatic Brain Injuries
• ISBN: 9783030117252
• DOI: https://doi.org/10.1007/978-3-030-11726-9_37
• Public URL: https://nottingham-repository.worktribe.com/output/2058382
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-030-11726-9_37
• Contract Date: May 16, 2019