用解剖结构指导点云表示，实现高效阿尔茨海默病分类
NeuroAPS-Net: Neuro-Anatomically Aware Point Cloud Representation for Efficient Alzheimer's Disease Classification
- 将MRI转为解剖优先的2D点云，构建首个标注脑区的点云数据集
- 模型在ADNI-2DPC上分类准确率领先，推理延迟降低58%，显存减少63%
- 适合资源受限场景，可解释性强，适用于神经退行性疾病分析
阿尔茨海默病（AD）是一种进行性神经退行性疾病，是痴呆的主要原因。结构磁共振成像（sMRI）被广泛用于分析与AD相关的脑萎缩；然而，大多数深度学习方法依赖计算成本高昂的3D卷积神经网络（CNN），限制了在资源受限环境中的部署。本文提出两项主要贡献：首先，我们设计了一条将T1加权MRI转换为解剖学感知的2D点云的流程，通过解剖优先采样（APS）生成了首个解剖标注的点云数据集——ADNI-2DPC；其次，提出NeuroAPS-Net，一种轻量级几何深度学习模型，通过区域感知特征编码和感兴趣区（ROI）令牌聚合引入解剖先验。在ADNI-2DPC上的实验表明，NeuroAPS-Net在保持竞争力分类准确率的同时，显著降低了推理延迟和GPU显存占用，相比当前最优点云方法表现更优。这些结果凸显了解剖引导点云学习在高效且可解释的AD分类中替代体素化CNN的潜力。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and a major cause of dementia. Structural MRI is widely used to analyze AD-related brain atrophy; however, most deep learning methods rely on computationally expensive 3D convolutional neural networks (CNNs), limiting deployment in resource-constrained settings. This work introduces two main contributions. First, we propose a pipeline that converts T1-weighted MRI into anatomically informed 2D point clouds using Anatomical Priority Sampling (APS), producing ADNI-2DPC, the first neuroanatomically labeled MRI-derived point cloud dataset. Second, we present NeuroAPS-Net, a lightweight geometric deep learning model that incorporates anatomical priors via region-aware feature encoding and ROI token aggregation. Experiments on ADNI-2DPC demonstrate that NeuroAPS-Net achieves competitive classification accuracy while significantly reducing inference latency and GPU memory compared to state-of-the-art point cloud methods. These results highlight the potential of anatomically guided point cloud learning as an efficient and interpretable alternative to voxel-based CNNs for AD classification.
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