Cargando…
Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder
Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4807075/ https://www.ncbi.nlm.nih.gov/pubmed/27057543 http://dx.doi.org/10.1155/2016/5284586 |
_version_ | 1782423339275386880 |
---|---|
author | Zhao, Guangjun Wang, Xuchu Niu, Yanmin Tan, Liwen Zhang, Shao-Xiang |
author_facet | Zhao, Guangjun Wang, Xuchu Niu, Yanmin Tan, Liwen Zhang, Shao-Xiang |
author_sort | Zhao, Guangjun |
collection | PubMed |
description | Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. |
format | Online Article Text |
id | pubmed-4807075 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-48070752016-04-07 Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder Zhao, Guangjun Wang, Xuchu Niu, Yanmin Tan, Liwen Zhang, Shao-Xiang Biomed Res Int Research Article Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. Hindawi Publishing Corporation 2016 2016-01-26 /pmc/articles/PMC4807075/ /pubmed/27057543 http://dx.doi.org/10.1155/2016/5284586 Text en Copyright © 2016 Guangjun Zhao et al. https://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 Zhao, Guangjun Wang, Xuchu Niu, Yanmin Tan, Liwen Zhang, Shao-Xiang Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder |
title | Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder |
title_full | Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder |
title_fullStr | Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder |
title_full_unstemmed | Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder |
title_short | Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder |
title_sort | segmenting brain tissues from chinese visible human dataset by deep-learned features with stacked autoencoder |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4807075/ https://www.ncbi.nlm.nih.gov/pubmed/27057543 http://dx.doi.org/10.1155/2016/5284586 |
work_keys_str_mv | AT zhaoguangjun segmentingbraintissuesfromchinesevisiblehumandatasetbydeeplearnedfeatureswithstackedautoencoder AT wangxuchu segmentingbraintissuesfromchinesevisiblehumandatasetbydeeplearnedfeatureswithstackedautoencoder AT niuyanmin segmentingbraintissuesfromchinesevisiblehumandatasetbydeeplearnedfeatureswithstackedautoencoder AT tanliwen segmentingbraintissuesfromchinesevisiblehumandatasetbydeeplearnedfeatureswithstackedautoencoder AT zhangshaoxiang segmentingbraintissuesfromchinesevisiblehumandatasetbydeeplearnedfeatureswithstackedautoencoder |