将脑部分区与分类解耦：阿尔茨海默病检测的快速脑分割方法的系统基准测试
Decoupling Parcellation from Classification: Systematic Benchmark of Fast Brain Segmentation Methods for Alzheimer's Disease Detection
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中文总结 AI 辅助
该研究将脑部分区与分类解耦，在OASIS-1数据集上对SynthSeg+等快速深度学习分区方法开展系统基准测试，评估多种体积策略与分类范式，以量化其对AD检测的性能。
中文摘要 AI 辅助
脑部分区与分类通常是单独评估的，但下游阿尔茨海默病（AD）检测性能取决于二者的相互作用。我们将这些组件解耦，在OASIS-1数据集上通过下游AD分类，对快速深度学习分区方法（SynthSeg+、OpenMAP-T1）与FreeSurfer（FS-HV）临床基线进行系统基准测试。我们的因子设计评估了三种分区方法、两种体积测量策略（硬策略与软策略），以及四种分类范式（临床阈值、监督前馈网络、集成方法和零/少样本提示的基础模型），所有结果均使用BCa Bootstrap 95%置信区间量化。
英文摘要
Brain parcellation and classification are typically evaluated in isolation, yet downstream AD detection performance depends on their interaction. We decouple these components and systematically benchmark fast deep learning parcellation methods (SynthSeg+, OpenMAP-T1) against the FreeSurfer (FS-HV) clinical baseline through down- stream AD classification on OASIS-1. Our factorial design evaluates three parcellation methods, two volumetry strategies (hard vs. soft), and four classifier paradigms (clinical thresholds, supervised feedforward networks, ensemble methods, and foundation models with zero/few-shot prompting), with all results quantified using BCa Bootstrap 95% confidence intervals.
发表机构
- Midea Group（美的集团）
机构由 AI 辅助整理，请以论文原文为准。
