Glioma Growth Prediction via Generative Adversarial Learning from Multi-Time Points Magnetic Resonance Images - Elazab, Ahmed; Wang, Changmiao; Safdar Gardezi, Syed Jamal; Bai, Hongmin; Wang, Tianfu; Lei, Baiying; Chang, Chunqi (Proceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society, 2020)
Gliomas are the most dominant and lethal type of brain tumors. Growth prediction is significant to quantify tumor aggressiveness, improve therapy planning, and estimate patients' survival time. This is commonly addressed in literature using mathematical models guided by multi-time point scans of multi/single-modal data for the same subject. However, these models are mechanism-based and heavily rely on complicated mathematical formulations of partial differential equations with few parameters that are insufficient to capture different patterns and other characteristics of gliomas. In this paper, we propose a 3D generative adversarial networks (GANs) for glioma growth prediction. Specifically, we stack 2 GANs with conditional initialization of segmented feature maps. Furthermore, we employ Dice loss in our objective function and devised 3D U-Net architecture for better image generation. The proposed method is trained and validated using 3D patch-based strategy on real magnetic resonance images of 9 subjects with 3 time points. Experimental results show that the proposed method can be successfully used for glioma growth prediction with satisfactory performance.