Multi-view prediction of Alzheimer's disease progression with end-to-end integrated framework
Multi-view prediction of Alzheimer's disease progression with end-to-end integrated framework
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DOI：
10.1016/j.jbi.2021.103978
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发表时间：
2021-12
影响因子：
4.5
通讯作者：
Yan Zhao;Baoqiang Ma;Tongtong Che;Qiongling Li;Debin Zeng;Xuetong Wang;Shuyu Li
中科院分区：
文献类型：
--
作者：
Yan Zhao;Baoqiang Ma;Tongtong Che;Qiongling Li;Debin Zeng;Xuetong Wang;Shuyu Li
阿尔茨海默病是一种常见的神经退行性脑部疾病，影响全球老年人口。其早期自动检测对早期干预和治疗至关重要。一种常见的解决方案是根据基线的脑结构磁共振图像(MRI)进行未来认知评分预测，这可以直接推断疾病的潜在严重程度。最近，几项研究通过预测未来的大脑MRI来模拟疾病的进展，这种MRI可以提供大脑随时间变化的视觉信息。然而，没有研究探讨这两种解决方案的内部相关性，也不知道预测的MRI是否可以帮助预测认知分数。在这里，我们的目标不是独立预测，而是多视角预测疾病进展，即同时预测特定对象的认知分数和MRI体积的变化。为了实现这一点，我们提出了一个端到端的集成框架，将回归模型和生成性对抗网络集成在一起，然后联合优化。三种集成策略被用来统一这两个模型。此外，考虑到一些大脑区域，如海马体和中回，可能在疾病进展过程中发生显著变化，在集成框架中引入感兴趣区(ROI)掩码和ROI丢失来利用这一解剖学先验知识。在纵向阿尔茨海默病神经成像计划数据集上的实验结果表明，集成框架在预测认知分数方面优于独立回归模型。并且随着认知分数和MRI预测的ROI损失，其性能可以进一步提高。
Alzheimer’s disease is a common neurodegenerative brain disease that affects the elderly population worldwide. Its early automatic detection is vital for early intervention and treatment. A common solution is to perform future cognitive score prediction based on the baseline brain structural magnetic resonance image (MRI), which can directly infer the potential severity of disease. Recently, several studies have modelled disease progression by predicting the future brain MRI that can provide visual information of brain changes over time. Nevertheless, no studies explore the intra correlation of these two solutions, and it is unknown whether the predicted MRI can assist the prediction of cognitive score. Here, instead of independent prediction, we aim to predict disease progression in multi-view, i.e., predicting subject-specific changes of cognitive score and MRI volume concurrently. To achieve this, we propose an end-to-end integrated framework, where a regression model and a generative adversarial network are integrated together and then jointly optimized. Three integration strategies are exploited to unify these two models. Moreover, considering that some brain regions, such as hippocampus and middle temporal gyrus, could change significantly during the disease progression, a region-of-interest (ROI) mask and a ROI loss are introduced into the integrated framework to leverage this anatomical prior knowledge. Experimental results on the longitudinal Alzheimer’s Disease Neuroimaging Initiative dataset demonstrated that the integrated framework outperformed the independent regression model for cognitive score prediction. And its performance can be further improved with the ROI loss for both cognitive score and MRI prediction.