Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation

Author:

Hao Yongfu1,Wang Tianyao2,Zhang Xinqing3,Duan Yunyun4,Yu Chunshui5,Jiang Tianzi1,Fan Yong1,

Affiliation:

1. Brainnetome Center; National Laboratory of Pattern Recognition; Institute of Automation, Chinese Academy of Sciences; Beijing China

2. Department of Radiology; Shanghai East Hospital; Shanghai China

3. Department of Neurology; Xuanwu Hospital of Capital Medical University; Beijing China

4. Department of Radiology; Xuanwu Hospital of Capital Medical University; Beijing China

5. Department of Radiology; Tianjin Medical University General Hospital; Tianjin China

Funder

National Basic Research Program of China (973 Program)

Hundred Talents Program of the Chinese Academy of Sciences

National Science Foundation of China

Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health)

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

AstraZeneca

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

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

Publisher

Wiley

Subject

Clinical Neurology,Neurology,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Anatomy

Reference82 articles.

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2. Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy;Aljabar;Neuroimage,2009

3. Effleient classifier generation and weighted voting for aflas-based segmentation: Two small steps faster and closer to the combination oracle;Artaechevarria;SPIE Med Imag,2008

4. Combination strategies in multi-atlas image segmentation: Application to brain MR data;Artaechevarria;IEEE Trans Image Process,2009

5. Unified segmentation;Ashburner;Neuroimage,2005

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