多模态2.5d卷积神经网络在磁共振成像和正电子发射断层扫描诊断阿尔茨海默病中的应用

Xuyang Zhang, Weiming Lin, Min Xiao, Huazhi Ji
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引用次数: 0

摘要

阿尔茨海默病(AD)是一种神经系统退行性疾病,常见于老年人。磁共振成像(MRI)和正电子发射断层扫描(PET)反映了AD引起的大脑解剖变化和功能变化,因此常被用于AD的诊断。基于这两类图像的多模态融合可以有效地利用互补信息,提高诊断性能。为了避免三维图像的计算复杂性,扩大训练样本,本研究设计了一个基于2.5D卷积神经网络(CNN)的AD诊断框架,融合多模态数据。首先,对MRI和PET进行颅骨剥离和配准预处理。然后在MRI和PET上提取海马区域内多个2.5D斑块。然后,我们构建了一个多模态2.5D CNN来整合来自mri和PET贴片的多模态信息。我们还采用分支预训练的训练策略,通过分别预训练具有相应模态的两个分支来增强2.5D CNN的特征提取能力。最后,使用贴片的结果来诊断AD和进行性轻度认知障碍(pMCI)患者,而不是正常对照(NC)。在ADNI数据集上进行实验,AD与NC、pMCI与NC任务的准确率分别达到92.89%和84.07%。结果表明,多模态2.5D CNN可以有效地整合多模态的互补信息,具有较好的AD诊断效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MULTIMODAL 2.5D CONVOLUTIONAL NEURAL NETWORK FOR DIAGNOSIS OF ALZHEIMER'S DISEASE WITH MAGNETIC RESONANCE IMAGING AND POSITRON EMISSION TOMOGRAPHY
Alzheimer’s disease (AD) is a degenerative disease of the nervous system that often occurs in the elderly. As magnetic resonance imaging (MRI) and positron emission tomography (PET) reflect the brain’s anatomical changes and functional changes caused by AD, they are often used to diagnose AD. Multimodal fusion based on these two types of images can effectively utilize complementary information and improve diagnostic performance. To avoid the computational complexity of the 3D image and expand training samples, this study designed an AD diagnosis framework based on a 2.5D convolutional neural network (CNN) to fuse multimodal data. First, MRI and PET were preprocessed with skull stripping and registration. After that, multiple 2.5D patches were extracted within the hippocampus regions from both MRI and PET. Then, we constructed a multimodal 2.5D CNN to integrate the multimodal information fromMRI and PET patches. We also utilized a training strategy called branches pre-training to enhance the feature extraction ability of the 2.5D CNN by pre-training two branches with corresponding modalities individually. Finally, the results of patches are used to diagnose AD and progressive mild cognitive impairment (pMCI) patients from normal controls (NC). The experiments were conducted with the ADNI dataset, and accuracies of 92.89% and 84.07% were achieved in the AD vs. NC and pMCI vs. NC tasks. The results are much better than using single modality and indicate that the proposed multimodal 2.5D CNN could effectively integrate complementary information from multi-modality and yield a promising AD diagnosis performance.
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