TY - GEN
T1 - Automatic hippocampus labeling using the hierarchy of sub-region random forests
AU - Zhang, Lichi
AU - Wang, Qian
AU - Gao, Yaozong
AU - Wu, Guorong
AU - Shen, Dinggang
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2015
Y1 - 2015
N2 - In this paper, we propose a multi-atlas-based framework for labeling hippocampus regions in the MR images. Our work aims at extending the random forests techniques for better performance, which contains two novel contributions: First, we design a novel strategy for training forests, to ensure that each forest is specialized in labeling the certain sub-region of the hippocampus in the images. In the testing stage, a novel approach is also presented for automatically finding the forests relevant to the corresponding sub-regions of the test image. Second, we present a novel localized registration strategy, which further reduces the shape variations of the hippocampus region in each atlas. This can provide better support for the proposed sub-region random forest approach. We validate the proposed framework on the ADNI dataset, in which atlases from NC, MCI and AD subjects are randomly selected for the experiments. The estimations demonstrated the validity of the proposed framework, showing that it yields better performances than the conventional random forests techniques.
AB - In this paper, we propose a multi-atlas-based framework for labeling hippocampus regions in the MR images. Our work aims at extending the random forests techniques for better performance, which contains two novel contributions: First, we design a novel strategy for training forests, to ensure that each forest is specialized in labeling the certain sub-region of the hippocampus in the images. In the testing stage, a novel approach is also presented for automatically finding the forests relevant to the corresponding sub-regions of the test image. Second, we present a novel localized registration strategy, which further reduces the shape variations of the hippocampus region in each atlas. This can provide better support for the proposed sub-region random forest approach. We validate the proposed framework on the ADNI dataset, in which atlases from NC, MCI and AD subjects are randomly selected for the experiments. The estimations demonstrated the validity of the proposed framework, showing that it yields better performances than the conventional random forests techniques.
UR - http://www.scopus.com/inward/record.url?scp=84955287465&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-28194-0_3
DO - 10.1007/978-3-319-28194-0_3
M3 - Conference contribution
AN - SCOPUS:84955287465
SN - 9783319281933
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 19
EP - 27
BT - Patch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers
A2 - Coupé, Pierrick
A2 - Munsell, Brent
A2 - Wu, Guorong
A2 - Zhan, Yiqiang
A2 - Rueckert, Daniel
PB - Springer Verlag
T2 - 1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015
Y2 - 9 October 2015 through 9 October 2015
ER -