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A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences

Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually wi...

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Autores principales: Wang, Haiyan, Han, Guoqiang, Li, Haojiang, Tao, Guihua, Zhuo, Enhong, Liu, Lizhi, Cai, Hongmin, Ou, Yangming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7474760/
https://www.ncbi.nlm.nih.gov/pubmed/32908581
http://dx.doi.org/10.1155/2020/7562140
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author Wang, Haiyan
Han, Guoqiang
Li, Haojiang
Tao, Guihua
Zhuo, Enhong
Liu, Lizhi
Cai, Hongmin
Ou, Yangming
author_facet Wang, Haiyan
Han, Guoqiang
Li, Haojiang
Tao, Guihua
Zhuo, Enhong
Liu, Lizhi
Cai, Hongmin
Ou, Yangming
author_sort Wang, Haiyan
collection PubMed
description Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC.
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spelling pubmed-74747602020-09-08 A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences Wang, Haiyan Han, Guoqiang Li, Haojiang Tao, Guihua Zhuo, Enhong Liu, Lizhi Cai, Hongmin Ou, Yangming Comput Math Methods Med Research Article Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC. Hindawi 2020-08-28 /pmc/articles/PMC7474760/ /pubmed/32908581 http://dx.doi.org/10.1155/2020/7562140 Text en Copyright © 2020 Haiyan Wang et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Haiyan
Han, Guoqiang
Li, Haojiang
Tao, Guihua
Zhuo, Enhong
Liu, Lizhi
Cai, Hongmin
Ou, Yangming
A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences
title A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences
title_full A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences
title_fullStr A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences
title_full_unstemmed A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences
title_short A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences
title_sort collaborative dictionary learning model for nasopharyngeal carcinoma segmentation on multimodalities mr sequences
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7474760/
https://www.ncbi.nlm.nih.gov/pubmed/32908581
http://dx.doi.org/10.1155/2020/7562140
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