用混合标记法加速大脑MRI的拓扑分析，10秒完成全脑处理。
Fast Witness Persistence for MRI Volumes via Hybrid Landmarking
- 混合度量筛选关键点，兼顾几何覆盖与密度均衡
- 相比随机或仅密度基方法，点间平均距离减少30%-60%
- 适合医疗影像研究者快速部署拓扑分析流程
我们提出一种可扩展的基于见证的持久同调流水线，用于全脑MRI体积分析，结合密度感知的关键点选择与适配GPU的见证过滤。候选点通过混合度量评分，平衡几何覆盖与逆核密度，使关键点集的平均成对距离比随机或仅密度基方法降低30%-60%，同时保持拓扑特征。在BrainWeb、IXI及合成流形上的基准测试仅需单张NVIDIA RTX 4090 GPU在十秒内完成，避免了Cech、Vietoris-Rips和alpha过滤的组合爆炸问题。该工具包以whale-tda命名发布于PyPI（可通过pip安装）；源码与问题追踪托管于https://github.com/jorgeLRW/whale。发布版本还提供快速预设（mri_deep_dive_fast）支持探索性扫描，并附带可复现脚本与资源，可直接集成至医学影像工作流。
原文摘要 · Abstract (English)
We introduce a scalable witness-based persistent homology pipeline for full-brain MRI volumes that couples density-aware landmark selection with a GPU-ready witness filtration. Candidates are scored by a hybrid metric that balances geometric coverage against inverse kernel density, yielding landmark sets that shrink mean pairwise distances by 30-60% over random or density-only baselines while preserving topological features. Benchmarks on BrainWeb, IXI, and synthetic manifolds execute in under ten seconds on a single NVIDIA RTX 4090 GPU, avoiding the combinatorial blow-up of Cech, Vietoris-Rips, and alpha filtrations. The package is distributed on PyPI as whale-tda (installable via pip); source and issues are hosted at https://github.com/jorgeLRW/whale. The release also exposes a fast preset (mri_deep_dive_fast) for exploratory sweeps, and ships with reproducibility-focused scripts and artifacts for drop-in use in medical imaging workflows.
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