| dc.description.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 Tu mor Segmentation (BraTS) challenge validation dataset, for tumour sub regions, we achieved a Dice similarity coefficient of 0.77 for both enhanc ing 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 evalua tion score that was the equal 5th highest value (with our method ranking
in 10th 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 | en |