GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images

• Published in: Neural networks Vol. 132; pp. 321 - 332
• Main Authors: Elazab, Ahmed, Wang, Changmiao, Gardezi, Syed Jamal Safdar, Bai, Hongmin, Hu, Qingmao, Wang, Tianfu, Chang, Chunqi, Lei, Baiying
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.12.2020
• Subjects: [formula omitted] and Dice losses; Brain - diagnostic imaging; Brain Neoplasms - diagnostic imaging; Forecasting; Gliomas; Growth prediction; Humans; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Longitudinal MR Images; Magnetic Resonance Imaging - methods; Neural Networks, Computer; Stacked 3D generative adversarial networks; l1 and Dice losses; Growth prediction; Longitudinal MR Images; Stacked 3D generative adversarial networks; Gliomas; and Dice losses

Brain tumors are one of the major common causes of cancer-related death, worldwide. Growth prediction of these tumors, particularly gliomas which are the most dominant type, can be quite useful to improve treatment planning, quantify tumor aggressiveness, and estimate patients’ survival time towards...