Decoupling Parcellation from Classification: Systematic Benchmark of Fast Brain Segmentation Methods for Alzheimer's Disease Detection

2026-08-16

AI Summary

This study addresses the opacity of Alzheimer’s disease (AD) detection mechanisms caused by coupled brain segmentation and classification. We propose decoupling these stages and systematically benchmarking rapid deep learning approaches. Through a factorial design, we comprehensively evaluate various combinations of segmentation methods—including SynthSeg+, OpenMAP-T1, and zero/few-shot prompting with foundation models—alongside volumetry and classifiers to assess their impact on downstream tasks. Extensive validation on the OASIS-1 dataset reveals critical interaction effects among components, with all results quantified using BCa bootstrap 95% confidence intervals. This work establishes a rigorous methodological foundation and performance benchmark for AD detection, clarifying the specific contributions of individual pipeline stages to diagnostic accuracy.

Abstract

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.

Problem

• Alzheimer's Disease Detection
• Brain Parcellation
• Benchmark
• Downstream Classification

Innovation

• Decoupling Parcellation and Classification
• Fast Brain Segmentation
• Systematic Benchmark
• Factorial Design
• Foundation Models