拆解脑区分割与分类关系，系统评测阿尔茨海默病检测新方法
Decoupling Parcellation from Classification: Systematic Benchmark of Fast Brain Segmentation Methods for Alzheimer's Disease Detection
- 分离分割与分类流程，用因子设计评估多种组合效果
- SynthSeg+在体积计算上表现最佳，准确率提升至89.3%
- 零样本提示的基座模型适合临床部署，节省标注成本
脑区分割与分类通常独立评估，但下游阿尔茨海默病（AD）检测性能依赖二者交互。本文将两者解耦，系统评估三种快速深度学习分割方法（SynthSeg+、OpenMAP-T1）与传统临床基准FreeSurfer（FS-HV）在OASIS-1数据集上的表现。采用因子设计，考察三种分割方法、两种体积计算策略（硬阈值/软阈值）及四种分类范式（临床阈值、监督前馈网络、集成方法、基于零/少样本提示的基座模型），所有结果均使用BCa Bootstrap 95%置信区间量化。结果显示，SynthSeg+结合软体积计算和基座模型，在分类准确率上达到89.3%，显著优于传统方法；且零样本提示策略在小样本场景下表现稳健。
原文摘要 · Abstract (English)
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.
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