3D-Conditional Generative Adversarial Networks for Brain Tumour Segmentation

Abstract

Gliomas, the most common primary brain tumor, exhibit significant heterogeneity in their prognosis, aggressiveness, and histological composition, encompassing areas such as necrotic cores, enhancing and non-enhancing tumor cores, and peritumoral edema. While multimodal MRI is invaluable for brain tumor detection, precise tumor segmentation remains challenging. To overcome this, a novel 3D volume-to-volume GAN, termed the 3D-Conditional Generative Adversarial Network (3D-cGAN), was developed for the brain tumor segmentation, leveraging data from the 2020 BraTS Challenge. This model utilizes multi channel 3D MRI images to accurately segment core, whole, and enhancing tumor regions. Employing a batch size of 4 and an alpha value of 2, the model demonstrates remarkable accuracy on the BraTS 2020 dataset, achieving a Dice score of 0.8286 and IoU score of 0.7111. © 2024 IEEE.