计算机科学
发电机(电路理论)
人工智能
特征(语言学)
模式识别(心理学)
生成语法
生成对抗网络
生成模型
人口
机器学习
图像(数学)
医学
物理
哲学
环境卫生
功率(物理)
量子力学
语言学
作者
Ahmed Elazab,Changmiao Wang,Syed Jamal Safdar Gardezi,Hongmin Bai,Qingmao Hu,Tianfu Wang,Chunqi Chang,Baiying Lei
标识
DOI:10.1016/j.neunet.2020.09.004
摘要
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 precision medicine. Studying tumor growth prediction basically requires multiple time points of single or multimodal medical images of the same patient. Recent models are based on complex mathematical formulations that basically rely on a system of partial differential equations, e.g. reaction diffusion model, to capture the diffusion and proliferation of tumor cells in the surrounding tissue. However, these models usually have small number of parameters that are insufficient to capture different patterns and other characteristics of the tumors. In addition, such models consider tumor growth independently for each subject, not being able to get benefit from possible common growth patterns existed in the whole population under study. In this paper, we propose a novel data-driven method via stacked 3D generative adversarial networks (GANs), named GP-GAN, for growth prediction of glioma. Specifically, we use stacked conditional GANs with a novel objective function that includes both l1 and Dice losses. Moreover, we use segmented feature maps to guide the generator for better generated images. Our generator is designed based on a modified 3D U-Net architecture with skip connections to combine hierarchical features and thus have a better generated image. The proposed method is trained and tested on 18 subjects with 3 time points (9 subjects from collaborative hospital and 9 subjects from BRATS 2014 dataset). Results show that our proposed GP-GAN outperforms state-of-the-art methods for glioma growth prediction and attain average Jaccard index and Dice coefficient of 78.97% and 88.26%, respectively.
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