Morphological Feature Visualization of Alzheimer's Disease via Multidirectional Perception GAN
Morphological Feature Visualization of Alzheimer's Disease via Multidirectional Perception GAN
复制标题
通过多向感知 GAN 实现阿尔茨海默病的形态特征可视化
DOI：
10.1109/tnnls.2021.3118369
复制
发表时间：
2022-03-23
影响因子：
10.4
通讯作者：
Ng, Michael K.
中科院分区：
文献类型：
--
作者：
Yu, Wen;Lei, Baiying;Ng, Michael K.
阿尔茨海默病（AD）的早期诊断对于及时治疗以减缓进一步恶化至关重要。观察AD早期的形态学特征具有重要的临床价值。在这项工作中，提出了一种新的多向感知生成对抗网络（MP-GAN），以可视化的形态特征，指示不同阶段的患者的AD的严重程度。具体而言，通过引入一种新的多方向映射机制到模型中，所提出的MP-GAN可以有效地捕获显着的全局特征。因此，使用从发生器的类判别地图，所提出的模型可以清楚地描绘细微病变通过MR图像之间的变换的源域和预定义的目标域。此外，通过整合对抗损失、分类损失、循环一致性损失和L1惩罚，MP-GAN中的单个生成器可以学习多个类的类判别映射。在阿尔茨海默病神经影像学倡议（ADNI）数据集上的大量实验结果表明，MP-GAN与现有方法相比具有上级性能. MP-GAN可视化的病变也与临床医生观察到的一致。
The diagnosis of early stages of Alzheimer's disease (AD) is essential for timely treatment to slow further deterioration. Visualizing the morphological features for early stages of AD is of great clinical value. In this work, a novel multidirectional perception generative adversarial network (MP-GAN) is proposed to visualize the morphological features indicating the severity of AD for patients of different stages. Specifically, by introducing a novel multidirectional mapping mechanism into the model, the proposed MP-GAN can capture the salient global features efficiently. Thus, using the class discriminative map from the generator, the proposed model can clearly delineate the subtle lesions via MR image transformations between the source domain and the predefined target domain. Besides, by integrating the adversarial loss, classification loss, cycle consistency loss, and L1 penalty, a single generator in MP-GAN can learn the class discriminative maps for multiple classes. Extensive experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that MP-GAN achieves superior performance compared with the existing methods. The lesions visualized by MP-GAN are also consistent with what clinicians observe.