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Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields
Because of the large variabilities in brain tumors, automating segmentation remains a difficult task. We propose an automated method to segment brain tumors by integrating the deep capsule network (CapsNet) and the latent-dynamic condition random field (LDCRF). The method consists of three main proc...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322984/ https://www.ncbi.nlm.nih.gov/pubmed/35877634 http://dx.doi.org/10.3390/jimaging8070190 |
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author | Elmezain, Mahmoud Mahmoud, Amena Mosa, Diana T. Said, Wael |
author_facet | Elmezain, Mahmoud Mahmoud, Amena Mosa, Diana T. Said, Wael |
author_sort | Elmezain, Mahmoud |
collection | PubMed |
description | Because of the large variabilities in brain tumors, automating segmentation remains a difficult task. We propose an automated method to segment brain tumors by integrating the deep capsule network (CapsNet) and the latent-dynamic condition random field (LDCRF). The method consists of three main processes to segment the brain tumor—pre-processing, segmentation, and post-processing. In pre-processing, the N4ITK process involves correcting each MR image’s bias field before normalizing the intensity. After that, image patches are used to train CapsNet during the segmentation process. Then, with the CapsNet parameters determined, we employ image slices from an axial view to learn the LDCRF-CapsNet. Finally, we use a simple thresholding method to correct the labels of some pixels and remove small 3D-connected regions from the segmentation outcomes. On the BRATS 2015 and BRATS 2021 datasets, we trained and evaluated our method and discovered that it outperforms and can compete with state-of-the-art methods in comparable conditions. |
format | Online Article Text |
id | pubmed-9322984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93229842022-07-27 Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields Elmezain, Mahmoud Mahmoud, Amena Mosa, Diana T. Said, Wael J Imaging Article Because of the large variabilities in brain tumors, automating segmentation remains a difficult task. We propose an automated method to segment brain tumors by integrating the deep capsule network (CapsNet) and the latent-dynamic condition random field (LDCRF). The method consists of three main processes to segment the brain tumor—pre-processing, segmentation, and post-processing. In pre-processing, the N4ITK process involves correcting each MR image’s bias field before normalizing the intensity. After that, image patches are used to train CapsNet during the segmentation process. Then, with the CapsNet parameters determined, we employ image slices from an axial view to learn the LDCRF-CapsNet. Finally, we use a simple thresholding method to correct the labels of some pixels and remove small 3D-connected regions from the segmentation outcomes. On the BRATS 2015 and BRATS 2021 datasets, we trained and evaluated our method and discovered that it outperforms and can compete with state-of-the-art methods in comparable conditions. MDPI 2022-07-08 /pmc/articles/PMC9322984/ /pubmed/35877634 http://dx.doi.org/10.3390/jimaging8070190 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Elmezain, Mahmoud Mahmoud, Amena Mosa, Diana T. Said, Wael Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields |
title | Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields |
title_full | Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields |
title_fullStr | Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields |
title_full_unstemmed | Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields |
title_short | Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields |
title_sort | brain tumor segmentation using deep capsule network and latent-dynamic conditional random fields |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322984/ https://www.ncbi.nlm.nih.gov/pubmed/35877634 http://dx.doi.org/10.3390/jimaging8070190 |
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