TY - JOUR
T1 - Learning of atlas forest hierarchy for automatic labeling of MR brain images
AU - Zhang, Lichi
AU - Wang, Qian
AU - Gao, Yaozong
AU - Wu, Guorong
AU - Shen, Dinggang
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - We propose a multi-atlas-based framework to label brain anatomies in magnetic resonance (MR) images, by constructing a hierarchical structure of atlas forests. We start by training the atlas forests in accordance to individual atlases, and then cluster atlas forests with similar labeling performances into several groups. For each group, a new representative forest is re-trained, based on all atlas images associated with the atlas forests in the group, as well as the tentative label maps output by the clustered atlas forests. This clustering and re-training procedure is conducted iteratively to obtain a hierarchical structure of atlas forests. When applied to an unlabeled image for testing, only the suitable trained atlas forests will be selected from the hierarchical structure. Hence the labeling result of the test image is fused from the outputs of selected atlas forests. Experimental results show that the proposed framework can significantly improve the labeling performance compared to the state-of-the-art method.
AB - We propose a multi-atlas-based framework to label brain anatomies in magnetic resonance (MR) images, by constructing a hierarchical structure of atlas forests. We start by training the atlas forests in accordance to individual atlases, and then cluster atlas forests with similar labeling performances into several groups. For each group, a new representative forest is re-trained, based on all atlas images associated with the atlas forests in the group, as well as the tentative label maps output by the clustered atlas forests. This clustering and re-training procedure is conducted iteratively to obtain a hierarchical structure of atlas forests. When applied to an unlabeled image for testing, only the suitable trained atlas forests will be selected from the hierarchical structure. Hence the labeling result of the test image is fused from the outputs of selected atlas forests. Experimental results show that the proposed framework can significantly improve the labeling performance compared to the state-of-the-art method.
UR - http://www.scopus.com/inward/record.url?scp=84921813142&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84921813142&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10581-9_40
DO - 10.1007/978-3-319-10581-9_40
M3 - Article
AN - SCOPUS:84921813142
VL - 8679
SP - 323
EP - 330
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SN - 0302-9743
ER -