A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images
Objectives This study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone‐beam computed tomography (CBCT) images using a deep learning artificial intelligence (AI) system. Setting and Sample Population Archives of the CBCT images were reviewed, and the data of 306 subj...
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| Veröffentlicht in: | Orthodontics & craniofacial research Jg. 24; H. S2; S. 117 - 123 |
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| Sprache: | Englisch |
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England
Wiley Subscription Services, Inc
01.12.2021
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| ISSN: | 1601-6335, 1601-6343, 1601-6343 |
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| Abstract | Objectives
This study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone‐beam computed tomography (CBCT) images using a deep learning artificial intelligence (AI) system.
Setting and Sample Population
Archives of the CBCT images were reviewed, and the data of 306 subjects with the pharyngeal airway were included in this retrospective study.
Material and Methods
A machine learning algorithm, based on Convolutional Neural Network (CNN), did the segmentation of the pharyngeal airway on serial CBCT images. Semi‐automatic software (ITK‐SNAP) was used to manually generate the airway, and the results were compared with artificial intelligence. Dice similarity coefficient (DSC) and Intersection over Union (IoU) were used as the accuracy of segmentation in comparing the measurements of human measurements and artificial intelligence algorithms.
Results
The human observer found the average volume of the pharyngeal airway to be 18.08 cm3 and artificial intelligence to be 17.32 cm3. For pharyngeal airway segmentation, a dice ratio of 0.919 and a weighted IoU of 0.993 is achieved.
Conclusions
In this study, a successful AI algorithm that automatically segments the pharyngeal airway from CBCT images was created. It can be useful in the quick and easy calculation of pharyngeal airway volume from CBCT images for clinical application. |
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| AbstractList | ObjectivesThis study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone‐beam computed tomography (CBCT) images using a deep learning artificial intelligence (AI) system.Setting and Sample PopulationArchives of the CBCT images were reviewed, and the data of 306 subjects with the pharyngeal airway were included in this retrospective study.Material and MethodsA machine learning algorithm, based on Convolutional Neural Network (CNN), did the segmentation of the pharyngeal airway on serial CBCT images. Semi‐automatic software (ITK‐SNAP) was used to manually generate the airway, and the results were compared with artificial intelligence. Dice similarity coefficient (DSC) and Intersection over Union (IoU) were used as the accuracy of segmentation in comparing the measurements of human measurements and artificial intelligence algorithms.ResultsThe human observer found the average volume of the pharyngeal airway to be 18.08 cm3 and artificial intelligence to be 17.32 cm3. For pharyngeal airway segmentation, a dice ratio of 0.919 and a weighted IoU of 0.993 is achieved.ConclusionsIn this study, a successful AI algorithm that automatically segments the pharyngeal airway from CBCT images was created. It can be useful in the quick and easy calculation of pharyngeal airway volume from CBCT images for clinical application. This study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone-beam computed tomography (CBCT) images using a deep learning artificial intelligence (AI) system.OBJECTIVESThis study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone-beam computed tomography (CBCT) images using a deep learning artificial intelligence (AI) system.Archives of the CBCT images were reviewed, and the data of 306 subjects with the pharyngeal airway were included in this retrospective study.SETTING AND SAMPLE POPULATIONArchives of the CBCT images were reviewed, and the data of 306 subjects with the pharyngeal airway were included in this retrospective study.A machine learning algorithm, based on Convolutional Neural Network (CNN), did the segmentation of the pharyngeal airway on serial CBCT images. Semi-automatic software (ITK-SNAP) was used to manually generate the airway, and the results were compared with artificial intelligence. Dice similarity coefficient (DSC) and Intersection over Union (IoU) were used as the accuracy of segmentation in comparing the measurements of human measurements and artificial intelligence algorithms.MATERIAL AND METHODSA machine learning algorithm, based on Convolutional Neural Network (CNN), did the segmentation of the pharyngeal airway on serial CBCT images. Semi-automatic software (ITK-SNAP) was used to manually generate the airway, and the results were compared with artificial intelligence. Dice similarity coefficient (DSC) and Intersection over Union (IoU) were used as the accuracy of segmentation in comparing the measurements of human measurements and artificial intelligence algorithms.The human observer found the average volume of the pharyngeal airway to be 18.08 cm3 and artificial intelligence to be 17.32 cm3 . For pharyngeal airway segmentation, a dice ratio of 0.919 and a weighted IoU of 0.993 is achieved.RESULTSThe human observer found the average volume of the pharyngeal airway to be 18.08 cm3 and artificial intelligence to be 17.32 cm3 . For pharyngeal airway segmentation, a dice ratio of 0.919 and a weighted IoU of 0.993 is achieved.In this study, a successful AI algorithm that automatically segments the pharyngeal airway from CBCT images was created. It can be useful in the quick and easy calculation of pharyngeal airway volume from CBCT images for clinical application.CONCLUSIONSIn this study, a successful AI algorithm that automatically segments the pharyngeal airway from CBCT images was created. It can be useful in the quick and easy calculation of pharyngeal airway volume from CBCT images for clinical application. Objectives This study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone‐beam computed tomography (CBCT) images using a deep learning artificial intelligence (AI) system. Setting and Sample Population Archives of the CBCT images were reviewed, and the data of 306 subjects with the pharyngeal airway were included in this retrospective study. Material and Methods A machine learning algorithm, based on Convolutional Neural Network (CNN), did the segmentation of the pharyngeal airway on serial CBCT images. Semi‐automatic software (ITK‐SNAP) was used to manually generate the airway, and the results were compared with artificial intelligence. Dice similarity coefficient (DSC) and Intersection over Union (IoU) were used as the accuracy of segmentation in comparing the measurements of human measurements and artificial intelligence algorithms. Results The human observer found the average volume of the pharyngeal airway to be 18.08 cm3 and artificial intelligence to be 17.32 cm3. For pharyngeal airway segmentation, a dice ratio of 0.919 and a weighted IoU of 0.993 is achieved. Conclusions In this study, a successful AI algorithm that automatically segments the pharyngeal airway from CBCT images was created. It can be useful in the quick and easy calculation of pharyngeal airway volume from CBCT images for clinical application. This study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone-beam computed tomography (CBCT) images using a deep learning artificial intelligence (AI) system. Archives of the CBCT images were reviewed, and the data of 306 subjects with the pharyngeal airway were included in this retrospective study. A machine learning algorithm, based on Convolutional Neural Network (CNN), did the segmentation of the pharyngeal airway on serial CBCT images. Semi-automatic software (ITK-SNAP) was used to manually generate the airway, and the results were compared with artificial intelligence. Dice similarity coefficient (DSC) and Intersection over Union (IoU) were used as the accuracy of segmentation in comparing the measurements of human measurements and artificial intelligence algorithms. The human observer found the average volume of the pharyngeal airway to be 18.08 cm and artificial intelligence to be 17.32 cm . For pharyngeal airway segmentation, a dice ratio of 0.919 and a weighted IoU of 0.993 is achieved. In this study, a successful AI algorithm that automatically segments the pharyngeal airway from CBCT images was created. It can be useful in the quick and easy calculation of pharyngeal airway volume from CBCT images for clinical application. |
| Author | Akkaya, Nurullah Öz, Ulaş Sin, Çağla Aksoy, Seçil Orhan, Kaan |
| Author_xml | – sequence: 1 givenname: Çağla orcidid: 0000-0002-5844-9404 surname: Sin fullname: Sin, Çağla email: cagla.sin@neu.edu.tr organization: Near East University – sequence: 2 givenname: Nurullah surname: Akkaya fullname: Akkaya, Nurullah organization: Near East University – sequence: 3 givenname: Seçil surname: Aksoy fullname: Aksoy, Seçil organization: Near East University – sequence: 4 givenname: Kaan surname: Orhan fullname: Orhan, Kaan organization: Ankara University – sequence: 5 givenname: Ulaş orcidid: 0000-0002-5203-577X surname: Öz fullname: Öz, Ulaş organization: Near East University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33619828$$D View this record in MEDLINE/PubMed |
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This study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone‐beam computed tomography (CBCT) images using a deep... This study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone-beam computed tomography (CBCT) images using a deep learning... ObjectivesThis study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone‐beam computed tomography (CBCT) images using a deep... |
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| SubjectTerms | Algorithms Artificial Intelligence Computed tomography Cone-Beam Computed Tomography Deep Learning Humans Image processing Image Processing, Computer-Assisted Itk protein Learning algorithms Machine learning Neural networks pharyngeal airway Pharynx Respiratory tract Retrospective Studies Segmentation Spiral Cone-Beam Computed Tomography |
| Title | A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images |
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