Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review
This paper provides an in-depth overview of Deep Neural Networks and their application in the segmentation and analysis of lung Magnetic Resonance Imaging (MRI) scans, specifically focusing on hyperpolarized gas MRI and the quantification of lung ventilation defects. An in-depth understanding of Dee...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/12/1349 |
| _version_ | 1827575638279585792 |
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| author | Ramtin Babaeipour Alexei Ouriadov Matthew S. Fox |
| author_facet | Ramtin Babaeipour Alexei Ouriadov Matthew S. Fox |
| author_sort | Ramtin Babaeipour |
| collection | DOAJ |
| description | This paper provides an in-depth overview of Deep Neural Networks and their application in the segmentation and analysis of lung Magnetic Resonance Imaging (MRI) scans, specifically focusing on hyperpolarized gas MRI and the quantification of lung ventilation defects. An in-depth understanding of Deep Neural Networks is presented, laying the groundwork for the exploration of their use in hyperpolarized gas MRI and the quantification of lung ventilation defects. Five distinct studies are examined, each leveraging unique deep learning architectures and data augmentation techniques to optimize model performance. These studies encompass a range of approaches, including the use of 3D Convolutional Neural Networks, cascaded U-Net models, Generative Adversarial Networks, and nnU-net for hyperpolarized gas MRI segmentation. The findings highlight the potential of deep learning methods in the segmentation and analysis of lung MRI scans, emphasizing the need for consensus on lung ventilation segmentation methods. |
| first_indexed | 2024-03-08T20:59:53Z |
| format | Article |
| id | doaj.art-9af8a15f715641059f136c8646ac8786 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-08T20:59:53Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-9af8a15f715641059f136c8646ac87862023-12-22T13:53:58ZengMDPI AGBioengineering2306-53542023-11-011012134910.3390/bioengineering10121349Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A ReviewRamtin Babaeipour0Alexei Ouriadov1Matthew S. Fox2School of Biomedical Engineering, Faculty of Engineering, The University of Western Ontario, London, ON N6A 3K7, CanadaSchool of Biomedical Engineering, Faculty of Engineering, The University of Western Ontario, London, ON N6A 3K7, CanadaDepartment of Physics and Astronomy, The University of Western Ontario, London, ON N6A 3K7, CanadaThis paper provides an in-depth overview of Deep Neural Networks and their application in the segmentation and analysis of lung Magnetic Resonance Imaging (MRI) scans, specifically focusing on hyperpolarized gas MRI and the quantification of lung ventilation defects. An in-depth understanding of Deep Neural Networks is presented, laying the groundwork for the exploration of their use in hyperpolarized gas MRI and the quantification of lung ventilation defects. Five distinct studies are examined, each leveraging unique deep learning architectures and data augmentation techniques to optimize model performance. These studies encompass a range of approaches, including the use of 3D Convolutional Neural Networks, cascaded U-Net models, Generative Adversarial Networks, and nnU-net for hyperpolarized gas MRI segmentation. The findings highlight the potential of deep learning methods in the segmentation and analysis of lung MRI scans, emphasizing the need for consensus on lung ventilation segmentation methods.https://www.mdpi.com/2306-5354/10/12/1349deep learningMagnetic Resonance Imaging (MRI)hyperpolarized gas MRIsegmentationventilation defectchronic obstructive pulmonary disease (COPD) |
| spellingShingle | Ramtin Babaeipour Alexei Ouriadov Matthew S. Fox Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review Bioengineering deep learning Magnetic Resonance Imaging (MRI) hyperpolarized gas MRI segmentation ventilation defect chronic obstructive pulmonary disease (COPD) |
| title | Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review |
| title_full | Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review |
| title_fullStr | Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review |
| title_full_unstemmed | Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review |
| title_short | Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review |
| title_sort | deep learning approaches for quantifying ventilation defects in hyperpolarized gas magnetic resonance imaging of the lung a review |
| topic | deep learning Magnetic Resonance Imaging (MRI) hyperpolarized gas MRI segmentation ventilation defect chronic obstructive pulmonary disease (COPD) |
| url | https://www.mdpi.com/2306-5354/10/12/1349 |
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