DeepAI AI Chat
Log In Sign Up

Brain tumour segmentation using a triplanar ensemble of U-Nets

05/24/2021
by   Vaanathi Sundaresan, et al.
0

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 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.

READ FULL TEXT

page 4

page 7

page 10

12/19/2022

Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution

Automatic segmentation is essential for the brain tumor diagnosis, disea...
02/23/2020

Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization

We introduce a novel ensembling method, Random Bundle (RB), that improve...
06/26/2019

Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations

Objective: Overlapping measures are often utilized to quantify the simil...
09/21/2022

Automated segmentation of intracranial hemorrhages from 3D CT

Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a ...