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, 24.36 mm, and 18 95 mm for Hausdorff distance 95 percentile for the BraTS testing set after ensembling 10 Vox2Vox models obtained with a 10-fold cross-validation. The code is available at https://github. com/mdciri/Vox2Vox.

Automated summary

The study presents a 3D volume-to-volume Generative Adversarial Network model for brain tumor segmentation, which achieved good segmentation results. The model accurately segments the whole, core, and enhancing tumor, providing potential significance for the diagnosis and treatment of brain tumors.