OpenBrain release rates whole brain MRI parcellation confidence
OpenBrain: An Auditable Generated-Label Release for Whole-Brain MRI Parcellation
Computer Vision and Pattern Recognition
Summary
Whole-brain MRI images can be automatically divided into regions, but it's hard to know how reliable these automatic labels are. The authors created OpenBrain, a large release of over 35,000 brain images with detailed confidence scores and quality checks for each case. This lets users pick or trust brain segmentations based on clear evidence without redoing the labeling. Tests show that focusing on more reliable cases improves accuracy in training brain analysis models. OpenBrain also provides full data records so users can verify and understand confidence levels after downloading.
What this means in practice
- •For medical image analysis teams: Use OpenBrain confidence scores to select reliable brain segmentations for training and validating brain analysis algorithms.
- •For machine learning engineers: Improve model training in neuroimaging tasks by prioritizing lower-risk MRI parcellations informed by OpenBrain’s quality indicators.
Authors
Qizhen Lan, Yu-Chun Hsu, Yuxiang Wei, Lijing Zhu, Zenan Sun, Liang He, Lishan Yu, Xiaoqian Jiang
Abstract
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.