101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol

TLDR

The Mindboggle‑101 dataset, the largest publicly available set of manually labeled human brain images, is introduced to serve as atlases, a normative reference for morphometric variation, and a resource for developing and evaluating automated cortical labeling algorithms. A new cortical labeling protocol, the Desikan‑Killiany‑Tourville (DKT) scheme, uses robust anatomical landmarks and minimal manual edits after automated initialization, and the dataset includes benchmarks comparing these manual labels to probabilistic and multi‑atlas registration methods. All data, software, and updated information are freely accessible at http://mindboggle.info/data.

Abstract

We introduce the Mindboggle-101 dataset, the largest and most complete set of free, publicly accessible, manually labeled human brain images. To manually label the macroscopic anatomy in magnetic resonance images of 101 healthy participants, we created a new cortical labeling protocol that relies on robust anatomical landmarks and minimal manual edits after initialization with automated labels. The "Desikan-Killiany-Tourville" (DKT) protocol is intended to improve the ease, consistency, and accuracy of labeling human cortical areas. Given how difficult it is to label brains, the Mindboggle-101 dataset is intended to serve as brain atlases for use in labeling other brains, as a normative dataset to establish morphometric variation in a healthy population for comparison against clinical populations, and contribute to the development, training, testing, and evaluation of automated registration and labeling algorithms. To this end, we also introduce benchmarks for the evaluation of such algorithms by comparing our manual labels with labels automatically generated by probabilistic and multi-atlas registration-based approaches. All data and related software and updated information are available on the http://mindboggle.info/data website.