Brain Tumour Segmentation Using a Triplanar Ensemble of U-Nets on MR Images

Abstract

Gliomas appear with wide variation in their characteristics both in terms of their appearance and location on brain MR images, which makes robust tumour segmentation highly challenging, and leads to high inter-rater variability even in manual segmentations. In this work, we propose a triplanar ensemble network, with an independent tumour core prediction module, for accurate segmentation of these tumours and their sub-regions. On evaluating our method on the MICCAI Brain Tumor Segmentation (BraTS) challenge validation dataset, for tumour sub-regions, we achieved a Dice similarity coefficient of 0.77 for both enhancing tumour (ET) and tumour core (TC). In the case of the whole tumour (WT) region, we achieved a Dice value of 0.89, which is on par with the top-ranking methods from BraTS’17-19. Our method achieved an evaluation score that was the equal 5$^{\text {th}}$ highest value (with our method ranking in 10$^{\text {th}}$ place) in the BraTS’20 challenge, with mean Dice values of 0.81, 0.89 and 0.84 on ET, WT and TC regions respectively on the BraTS’20 unseen test dataset.

L. Griffanti and M. Jenkinson—Contributed equally to this work.