Automatic labeling of MR brain images by hierarchical learning of atlas forests: Automatic labeling of MR brain images
Year: 2020
Type: article
Abstract: Automatic brain image labeling is highly demanded in the field of medical image analysis. Multiatlas-based approaches are widely used due to their simplicity and robustness in applications. Also, random forest technique is recognized as an efficient method for labeling, although there are several existing limitations. In this paper, the authors intend to address those limitations by proposing a novel framework based on the hierarchical learning of atlas forests.
DOI: 10.17615/5xka-nb67
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1600120695, G1610907132, G1666955300, G3139536118, G3712795371, Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment, G4739438220, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Longitudinal Mapping of Human Brain Development in the First Years of Life, Fast, Robust Analysis of Large Population Data, G6748413423, G6908867551, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI, G9000040056, G9000106553, G9000108347, G9000184678, G9000211028
OpenAlex ID: W4300653153