Open AccessGlioblastoma Synthesis and Segmentation with 3D Multi-Modal MRI: A Study using Generative Adversarial Networks
Author(s) -
Edmond Wang
Publication year - 2021
Publication title -
international journal on computational science and applications
Language(s) - English
Resource type - Journals
ISSN - 2200-0011
DOI - 10.5121/ijcsa.2021.11601
Subject(s) - segmentation , computer science , deep learning , artificial intelligence , generative grammar , modal , field (mathematics) , glioblastoma , machine learning , set (abstract data type) , generative adversarial network , pattern recognition (psychology) , medicine , chemistry , mathematics , cancer research , polymer chemistry , pure mathematics , programming language
The Grade IV cancer Glioblastoma is an extremely common and aggressive brain tumour. It is of significant consequence that histopathologic examinations should be able to identify and capture the tumour’s genetic variability for assistance in treatment. The use of Deep Learning - in particular CNNs and GANs - have become prominent in dealing with various image segmentation and detection tasks. The use of GANs have another importance - to expand the available training set by generating realistic pseudomedical images. Multi-modal MRIs, moreover, are also crucial as they lead to more successful performances. Nonetheless, accurate segmentation and realistic image synthesis remain challenging tasks. In this study, the history and various breakthroughs/challenges of utilising deep learning in glioblastoma detection is outlined and evaluated. To see networks in action, an adjusted and calibrated Vox2Vox network - a 3D implementation of the Pix2Pix translator - is trained on the biggest public brain tumour dataset BraTS 2020. The experimental results demonstrate the versatility and improvability of GAN networks in both fields of augmentation and segmentation. Overall, deep learning in medical imaging remains an extremely intoxicating field full of meticulous and innovative new studies.