Ultra-high resolution multimodal MRI dense labelled holistic brain atlas

2025-01-28

AI Summary

Existing human brain atlases suffer from limitations in spatial resolution, multimodal integration, and hierarchical anatomical granularity. Method: We introduce holiAtlas—the first ultra-high-resolution (0.125 mm³), whole-brain, densely labeled, multiscale, multimodal (T1/T2/WMn) human brain atlas. Built upon data from 75 healthy Human Connectome Project (HCP) subjects, it employs a novel dense labeling protocol integrating 10 local parcellation schemes to yield 350 anatomically grounded labels; multiscale clustering ensures cross-hierarchical anatomical consistency and scalability. Atlas construction leverages symmetric groupwise normalization registration, multi-contrast image fusion, and hierarchical semantic aggregation. Contribution/Results: holiAtlas is publicly released and enables development of super-resolution brain segmentation algorithms. Its unprecedented resolution and multimodal, hierarchical annotation provide a high-fidelity reference for identifying early neuroimaging biomarkers of neurodegenerative disorders.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

In this paper, we introduce holiAtlas, a holistic, multimodal and high-resolution human brain atlas. This atlas covers different levels of details of the human brain anatomy, from the organ to the substructure level, using a new dense labelled protocol generated from the fusion of multiple local protocols at different scales. This atlas has been constructed averaging images and segmentations of 75 healthy subjects from the Human Connectome Project database. Specifically, MR images of T1, T2 and WMn (White Matter nulled) contrasts at 0.125 $mm^{3}$ resolution that were nonlinearly registered and averaged using symmetric group-wise normalisation to construct the atlas. At the finest level, the holiAtlas protocol has 350 different labels derived from 10 different delineation protocols. These labels were grouped at different scales to provide a holistic view of the brain at different levels in a coherent and consistent manner. This multiscale and multimodal atlas can be used for the development of new ultra-high resolution segmentation methods that can potentially leverage the early detection of neurological disorders.

Problem

• High-definition Brain Mapping
• Multi-scale Analysis
• Multi-modal Imaging

Innovation

• holiAtlas
• multi-modal brain mapping
• high-resolution imaging