Vox2Vox: 3D-GAN for Brain Tumour Segmentation
This work addresses accurate segmentation of gliomas in medical imaging, which is crucial for diagnosis and treatment planning, but it appears incremental as it builds on existing GAN and segmentation methods.
The paper tackles brain tumor segmentation from multi-modal MRI by proposing Vox2Vox, a 3D volume-to-volume Generative Adversarial Network, achieving dice scores of 87.20%, 81.14%, and 78.67% for whole, core, and enhancing tumor segmentation on the BraTS 2020 testing set.
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.