Paper Info
Title
Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation.
Abstract
Quantitative magnetic resonance analysis often requires accurate, robust, and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert manual segmentations as priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. Validation with two different datasets is presented. In our experiments, the hippocampi of 80 healthy subjects and the lateral ventricles of 80 patients with Alzheimer's disease were segmented. The influence on segmentation accuracy of different parameters such as patch size and number of training subjects was also studied. A comparison with an appearance-based method and a template-based method was also carried out. The highest median kappa index values obtained with the proposed method were 0.884 for hippocampus segmentation and 0.959 for lateral ventricle segmentation.
Year
DOI
Venue
2011
10.1016/j.neuroimage.2010.09.018
NeuroImage
Keywords
Field
DocType
MRI,Brain,Hippocampus,Lateral ventricles,Alzheimer's disease,Image processing,Structure segmentation
Lateral ventricles,Computer vision,Scale-space segmentation,Segmentation,Image processing,Psychology,Segmentation-based object categorization,Image segmentation,Ventricle,Artificial intelligence,Prior probability
Journal
Volume
Issue
ISSN
54
2
1053-8119
Citations 
PageRank 
References 
249
7.84
31
Authors
6
Search Limit
100249
Name
Order
Citations
PageRank
Pierrick Coupé1120960.13
José V. Manjón279539.24
Vladimir Fonov347820.32
Jens C Pruessner450530.05
Montserrat Robles5106458.83
D. Louis Collins63915403.90