This article was published online on 23 October 2014. An error was subsequently identified. This notice is included in the online and print versions to indicate that both have been corrected 28 February 2014.
Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation
Version of Record online: 23 OCT 2013
Copyright © 2013 Wiley Periodicals, Inc.
Human Brain Mapping
Volume 35, Issue 6, pages 2674–2697, June 2014
How to Cite
Hao, Y., Wang, T., Zhang, X., Duan, Y., Yu, C., Jiang, T., Fan, Y. and for the Alzheimer's Disease Neuroimaging Initiative (2014), Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation. Hum. Brain Mapp., 35: 2674–2697. doi: 10.1002/hbm.22359
- Issue online: 21 APR 2014
- Version of Record online: 23 OCT 2013
- Manuscript Accepted: 17 JUN 2013
- Manuscript Received: 15 APR 2013
- National Basic Research Program of China (973 Program). Grant Number: 2011CB707801
- Hundred Talents Program of the Chinese Academy of Sciences
- National Science Foundation of China. Grant Numbers: 30970770, 91132707, 60831004
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health). Grant Number: U01 AG024904
- National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Amorfix Life Sciences Ltd
- Bayer HealthCare
- BioClinica, Inc.
- Biogen Idec Inc.
- Bristol-Myers Squibb Company
- Eisai Inc.
- Elan Pharmaceuticals Inc.
- Eli Lilly and Company
- F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.
- GE Healthcare
- Innogenetics, N.V
- Janssen Alzheimer Immunotherapy Research & Development, LLC
- Johnson & Johnson Pharmaceutical Research & Development LLC
- Medpace, Inc.
- Merck & Co., Inc.
- Meso Scale Diagnostics, LLC
- Novartis Pharmaceuticals Corporation
- Pfizer Inc; Servier; Synarc Inc.
- Takeda Pharmaceutical Company
- NIH. Grant Numbers: P30 AG010129, K01 AG030514
- Dana Foundation
- The Canadian Institutes of Health Research, Foundation for the National Institutes of Health (available at: www.fnih.org)
- Northern California Institute for Research and Education
- multi-atlas based segmentation;
- local label learning;
- hippocampal segmentation;
Automatic and reliable segmentation of subcortical structures is an important but difficult task in quantitative brain image analysis. Multi-atlas based segmentation methods have attracted great interest due to their promising performance. Under the multi-atlas based segmentation framework, using deformation fields generated for registering atlas images onto a target image to be segmented, labels of the atlases are first propagated to the target image space and then fused to get the target image segmentation based on a label fusion strategy. While many label fusion strategies have been developed, most of these methods adopt predefined weighting models that are not necessarily optimal. In this study, we propose a novel local label learning strategy to estimate the target image's segmentation label using statistical machine learning techniques. In particular, we use a L1-regularized support vector machine (SVM) with a k nearest neighbor (kNN) based training sample selection strategy to learn a classifier for each of the target image voxel from its neighboring voxels in the atlases based on both image intensity and texture features. Our method has produced segmentation results consistently better than state-of-the-art label fusion methods in validation experiments on hippocampal segmentation of over 100 MR images obtained from publicly available and in-house datasets. Volumetric analysis has also demonstrated the capability of our method in detecting hippocampal volume changes due to Alzheimer's disease. Hum Brain Mapp 35:2674–2697, 2014. © 2013 Wiley Periodicals, Inc.