Hippocampus Segmentation Based on Iterative Local Linear Mapping With Representative and Local Structure-Preserved Feature Embedding

Hippocampus segmentation plays a significant role in mental disease diagnoses, such as Alzheimer's disease, epilepsy, and so on. Patch-based multi-atlas segmentation (PBMAS) approach is a popular method for hippocampus segmentation and has achieved a promising result. However, the PBMAS approac...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 38; H. 10; S. 2271 - 2280
Hauptverfasser:	Pang, Shumao, Feng, Qianjin, Lu, Zhentai, Jiang, Jun, Zhao, Lei, Lin, Liyan, Li, Xueli, Lian, Tao, Huang, Meiyan, Yang, Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.10.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Alzheimer Disease - diagnostic imaging Constraints Datasets Deep Learning Dictionaries Diseases Embedding Epilepsy Feature extraction Hippocampus Hippocampus - diagnostic imaging Humans Image Interpretation, Computer-Assisted - methods Image processing Image reconstruction Image segmentation iterative local linear mapping Iterative methods Magnetic resonance Magnetic Resonance Imaging manifold regularization Manifolds Mapping Mental disorders multi-atlas segmentation Neuroimaging - methods Registration Regularization Segmentation Training
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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