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An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images
Medical image registration is an essential technique to achieve spatial consistency geometric positions of different medical images obtained from single- or multi-sensor, such as computed tomography (CT), magnetic resonance (MR), and ultrasound (US) images. In this paper, an improved unsupervised le...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8472627/ https://www.ncbi.nlm.nih.gov/pubmed/34577461 http://dx.doi.org/10.3390/s21186254 |
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author | Yang, Shaodi Zhao, Yuqian Liao, Miao Zhang, Fan |
author_facet | Yang, Shaodi Zhao, Yuqian Liao, Miao Zhang, Fan |
author_sort | Yang, Shaodi |
collection | PubMed |
description | Medical image registration is an essential technique to achieve spatial consistency geometric positions of different medical images obtained from single- or multi-sensor, such as computed tomography (CT), magnetic resonance (MR), and ultrasound (US) images. In this paper, an improved unsupervised learning-based framework is proposed for multi-organ registration on 3D abdominal CT images. First, the explored coarse-to-fine recursive cascaded network (RCN) modules are embedded into a basic U-net framework to achieve more accurate multi-organ registration results from 3D abdominal CT images. Then, a topology-preserving loss is added in the total loss function to avoid a distortion of the predicted transformation field. Four public databases are selected to validate the registration performances of the proposed method. The experimental results show that the proposed method is superior to some existing traditional and deep learning-based methods and is promising to meet the real-time and high-precision clinical registration requirements of 3D abdominal CT images. |
format | Online Article Text |
id | pubmed-8472627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84726272021-09-28 An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images Yang, Shaodi Zhao, Yuqian Liao, Miao Zhang, Fan Sensors (Basel) Article Medical image registration is an essential technique to achieve spatial consistency geometric positions of different medical images obtained from single- or multi-sensor, such as computed tomography (CT), magnetic resonance (MR), and ultrasound (US) images. In this paper, an improved unsupervised learning-based framework is proposed for multi-organ registration on 3D abdominal CT images. First, the explored coarse-to-fine recursive cascaded network (RCN) modules are embedded into a basic U-net framework to achieve more accurate multi-organ registration results from 3D abdominal CT images. Then, a topology-preserving loss is added in the total loss function to avoid a distortion of the predicted transformation field. Four public databases are selected to validate the registration performances of the proposed method. The experimental results show that the proposed method is superior to some existing traditional and deep learning-based methods and is promising to meet the real-time and high-precision clinical registration requirements of 3D abdominal CT images. MDPI 2021-09-18 /pmc/articles/PMC8472627/ /pubmed/34577461 http://dx.doi.org/10.3390/s21186254 Text en © 2021 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 Yang, Shaodi Zhao, Yuqian Liao, Miao Zhang, Fan An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images |
title | An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images |
title_full | An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images |
title_fullStr | An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images |
title_full_unstemmed | An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images |
title_short | An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images |
title_sort | unsupervised learning-based multi-organ registration method for 3d abdominal ct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8472627/ https://www.ncbi.nlm.nih.gov/pubmed/34577461 http://dx.doi.org/10.3390/s21186254 |
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