融合脑影像与形态特征，提升阿尔茨海默病分类和脑龄预测准确率。
Combining imaging and shape features for prediction tasks of Alzheimer's disease classification and brain age regression
- 用ResNet提取图像特征，图神经网络处理15个脑区表面网格的形状特征。
- 在CamCAN、IXI和OASIS3数据集上，分类任务性能显著提升。
- 适合关注脑结构形态与疾病关联的研究者和临床辅助诊断开发者。
本文研究将磁共振成像（MRI）中的影像特征与15个脑区表面网格提取的形态特征相结合，用于阿尔茨海默病分类和脑龄预测这两个临床相关任务。提出的模型通过ResNet提取图像嵌入，并利用定制的图神经网络生成形状嵌入，捕捉精细几何信息。结合来自T1加权图像的外观特征后，在脑龄预测和疾病分类任务中均取得性能提升，尤其在分类任务中增益显著。我们在CamCAN、IXI和OASIS3等公开数据集上验证了模型有效性，证明融合影像与形态特征对脑分析具有重要意义。
原文摘要 · Abstract (English)
We investigate combining imaging and shape features extracted from MRI for the clinically relevant tasks of brain age prediction and Alzheimer's disease classification. Our proposed model fuses ResNet-extracted image embeddings with shape embeddings from a bespoke graph neural network. The shape embeddings are derived from surface meshes of 15 brain structures, capturing detailed geometric information. Combined with the appearance features from T1-weighted images, we observe improvements in the prediction performance on both tasks, with substantial gains for classification. We evaluate the model using public datasets, including CamCAN, IXI, and OASIS3, demonstrating the effectiveness of fusing imaging and shape features for brain analysis.
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