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 tumor core. Although both these brain tumour types can easily be detected using multi-modal MRI, and exact them doing image 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, detecting the whole, core and enhancing tumor with median values of 93.39%, 92.50%, and 87.16% as dice scores and 2.44mm, 2.23mm, and 1.73mm for Hausdorff distance 95 percentile for the training dataset, and 91.75%, 88.13%, and 85.87% and 3.0mm, 3.74mm, and 2.23mm for the validation dataset, after ensembling 10 Vox2Vox models obtained with a 10-fold cross-validation.