A Deep Learning-based 3D-GAN for Glioma Subregions Detection and Segmentation in Multimodal Brain MRI volumes

Accurate, fast, and automatic gliomas segmentation from brain magnetic resonance imaging (MRI) scans is crucial for brain cancer diagnosis and therapy, but it remains, until nowadays, challenging because of variations in location, form, and imaging intensity of gliomas. This paper presents an automatic high- and low-grade glioma (HGG and LGG) sub-regions segmentation technique using deep learning-based three-dimensional generative adversarial network (3D-GAN) that localizes and segments the whole gliomas and their intra-regions, comprising necroses, edemas and active tumors, in multimodal brain MRI volumes of BraTS'2022 Datasets. Experimental tests and evaluations of the proposed GAN model have been realized on both, BraTS'2022 training and validation datasets, containing 5880 brain MRIs corresponding to 1470 different subjects with HGG and LGG of various sizes, forms, locations, and intensities. Dice coefficients for enhancing tumor (ET), whole tumor (WT), and tumor core (TC) reached 0.770, 0.872 and 0.832 respectively on 20 % of the training dataset. On the challenge validation dataset, our model achieved an average ET, WT, and TC Dice score of 0.774, 0.907 and 0.829 respectively.