4 research outputs found
Vox2Vox: 3D-GAN for Brain Tumour Segmentation
Gliomas are the most common primary brain malignancies, with different
degrees of aggressiveness, variable prognosis and various heterogeneous
histological sub-regions, i.e., peritumoral edema, necrotic core, enhancing and
non-enhancing tumour core. Although brain tumours can easily be detected using
multi-modal MRI, accurate tumor segmentation is a challenging task. Hence,
using the data provided by the BraTS Challenge 2020, we propose a 3D
volume-to-volume Generative Adversarial Network for segmentation of brain
tumours. The model, called Vox2Vox, generates realistic segmentation outputs
from multi-channel 3D MR images, segmenting the whole, core and enhancing tumor
with mean values of 87.20%, 81.14%, and 78.67% as dice scores and 6.44mm,
24.36mm, and 18.95mm for Hausdorff distance 95 percentile for the BraTS testing
set after ensembling 10 Vox2Vox models obtained with a 10-fold
cross-validation