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

arXiv 2608.16039 ↗First posted 17 Aug 2026eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

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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References · 14

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  10. 2020Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrastcited by 45
  11. 2018QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomycited by 37
  12. 2023Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Imagescited by 31
2 more
  1. 2022Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitionscited by 20
  2. 2023BDEC:Brain Deep Embedded Clustering modelcited by 1

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