[论文] OpenBrain: An Auditable Generated-Label Release for Whole-Brain MRI Pa...

论文概要

研究领域: CV 作者: Qizhen Lan, Yu-Chun Hsu, Yuxiang Wei, Lijing Zhu, Zenan Sun, Liang He et al. 发布时间: 2026-11-03 arXiv: 2611.05348

中文摘要

自动化工具如今可以在队列规模上生成全脑 MRI 分区，但生成的标签发布往往缺乏关于哪些病例可靠到可复用的证据。我们提出 OpenBrain——一个置信度感知的发布集，包含来自 607 个源数据集、经溯源验证的 35,838 例 OpenNeuro T1w 病例。OpenBrain 以可审计的病例记录形式组织，而非一堆独立分割文件：每条记录关联主分割、两个委员会分割、来源/许可溯源、QC-7 原始测量、百分位风险、综合风险分数 R 与置信度等级。QC-7 是一套免参考的可靠性面板，覆盖标签几何、图像-标签一致性与委员会分歧，用户可以在不重跑推理的情况下过滤、加权、审计或重新设定阈值。在冻结协议下评估：跨外部队列，更低的 QC-7 风险对应更高的生成标签质量；在固定预算训练中，选低风险样本比随机选择把配对宏 Dice 提高 +3.2 至 +3.7 个百分点。OpenBrain 还公开原始轴、委员会输出、溯源和数据表文档，使置信度声明在下载后仍可检查。它并不把生成标签变成人工标注或临床级分割；其贡献是：一个大规模开放的分区基底、可复用的病例级风险证据层，以及发布时 QC 的外部验证协议。

原文摘要

Automatic tools can now generate whole-brain MRI parcellations at cohort scale, but generated-label releases often provide little evidence about which cases are reliable enough to reuse. We introduce OpenBrain, a confidence-aware release of 35,838 provenance-verified OpenNeuro T1w cases from 607 source datasets. OpenBrain is organized as an auditable case-record release rather than a directory of standalone segmentation files: each record links a primary parcellation, two committee parcellations, source/license provenance, raw QC-7 measurements, percentile risks, a combined risk score R, and a confidence tier. QC-7 is a reference-free reliability panel over label geometry, image-label consistency, and committee disagreement, so users can filter, weight, audit, or re-threshold generated labels without rerunning inference. We evaluate this released evidence under a frozen protocol. Across external cohorts, lower QC-7 risk corresponds to higher generated-label quality; in fixed-budget training, lower-risk selections improve paired macro Dice over random selection by +3.2 to +3.7 percentage points. OpenBrain also exposes raw axes, committee outputs, provenance, and datasheet documentation, so confidence claims remain inspectable after download. It does not turn generated labels into manual annotations or clinical-grade segmentations. Its contribution is a large open generated-parcellation substrate, a reusable case-level risk-evidence layer, and an external validation protocol for release-time QC. Dataset: https://huggingface.co/datasets/lanqz7766/OpenBrain. Code: https://github.com/lanqz7766/openbrain.

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*自动采集于 2026-11-05*

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