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
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Zusammenfassung: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 approach needs high computation cost due to registration and the segmentation accuracy is subject to the registration accuracy. In this paper, we propose a novel method based on iterative local linear mapping (ILLM) with the representative and local structure-preserved feature embedding to achieve accurate and robust hippocampus segmentation with no need for registration. In the proposed approach, semi-supervised deep autoencoder (SSDA) exploits unsupervised deep autoencoder and local structure-preserved manifold regularization to nonlinearly transform the extracted magnetic resonance (MR) patch to embedded feature manifold, whose adjacent relationship is similar to the signed distance map (SDM) patch manifold. Local linear mapping is used to preliminarily predict SDM patch corresponding to the MR patch. Subsequently, threshold segmentation generates a preliminary segmentation. The ILLM refines the segmentation result iteratively by ensuring the local constraints of embedded feature manifold and SDM patch manifold using a space-constrained dictionary update. Thus, a refined segmentation is obtained with no need for registration. The experiments on 135 subjects from ADNI dataset show that the proposed approach is superior to the state-of-the-art PBMAS and classification-based approaches with mean Dice similarity coefficients of 0.8852 ± 0.0203 and 0.8783 ± 0.0251 for bilateral hippocampus segmentation of 1.5T and 3.0T datasets, respectively.
Bibliographie:ObjectType-Article-1
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2019.2906727