Automatic estimation of the aortic lumen geometry by ellipse tracking
Purpose The shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for extracting the main features of the aorta that may be used as a diagnostic aid for physicians. This article presents a new fully automated algori...
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| Published in: | International journal for computer assisted radiology and surgery Vol. 14; no. 2; pp. 345 - 355 |
|---|---|
| Main Authors: | , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Cham
Springer International Publishing
01.02.2019
Springer Nature B.V |
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| ISSN: | 1861-6410, 1861-6429, 1861-6429 |
| Online Access: | Get full text |
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| Abstract | Purpose
The shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for extracting the main features of the aorta that may be used as a diagnostic aid for physicians. This article presents a new fully automated algorithm to extract the aorta geometry for either normal (with and without contrast) or abnormal computed tomography (CT) cases.
Methods
The algorithm we propose is a fast incremental technique that computes the 3D geometry of the aortic lumen from an initial contour located inside it. Our approach is based on the optimization of the 3D orientation of the cross sections of the aorta. The method uses a robust ellipse estimation algorithm and an energy-based optimization technique to automatically track the centerline and the cross sections. The optimization involves the size and eccentricity of the ellipse which best fits the aorta contour on each cross-sectional plane. The method works directly on the original CT and does not require a prior segmentation of the aortic lumen. We present experimental results to show the accuracy of the method and its ability to cope with challenging CT cases where the aortic lumen may have low contrast, different kinds of pathologies, artifacts, and even significant angulations due to severe elongations.
Results
The algorithm correctly tracked the aorta geometry in 380 of 385 CT cases. The mean of the dice similarity coefficient was 0.951 for aorta cross sections that were randomly selected from the whole database. The mean distance to a manually delineated segmentation of the aortic lumen was 0.9 mm for sixteen selected cases.
Conclusions
The results achieved after the evaluation demonstrate that the proposed algorithm is robust and accurate for the automatic extraction of the aorta geometry for both normal (with and without contrast) and abnormal CT volumes. |
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| AbstractList | The shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for extracting the main features of the aorta that may be used as a diagnostic aid for physicians. This article presents a new fully automated algorithm to extract the aorta geometry for either normal (with and without contrast) or abnormal computed tomography (CT) cases.
The algorithm we propose is a fast incremental technique that computes the 3D geometry of the aortic lumen from an initial contour located inside it. Our approach is based on the optimization of the 3D orientation of the cross sections of the aorta. The method uses a robust ellipse estimation algorithm and an energy-based optimization technique to automatically track the centerline and the cross sections. The optimization involves the size and eccentricity of the ellipse which best fits the aorta contour on each cross-sectional plane. The method works directly on the original CT and does not require a prior segmentation of the aortic lumen. We present experimental results to show the accuracy of the method and its ability to cope with challenging CT cases where the aortic lumen may have low contrast, different kinds of pathologies, artifacts, and even significant angulations due to severe elongations.
The algorithm correctly tracked the aorta geometry in 380 of 385 CT cases. The mean of the dice similarity coefficient was 0.951 for aorta cross sections that were randomly selected from the whole database. The mean distance to a manually delineated segmentation of the aortic lumen was 0.9 mm for sixteen selected cases.
The results achieved after the evaluation demonstrate that the proposed algorithm is robust and accurate for the automatic extraction of the aorta geometry for both normal (with and without contrast) and abnormal CT volumes. PurposeThe shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for extracting the main features of the aorta that may be used as a diagnostic aid for physicians. This article presents a new fully automated algorithm to extract the aorta geometry for either normal (with and without contrast) or abnormal computed tomography (CT) cases.MethodsThe algorithm we propose is a fast incremental technique that computes the 3D geometry of the aortic lumen from an initial contour located inside it. Our approach is based on the optimization of the 3D orientation of the cross sections of the aorta. The method uses a robust ellipse estimation algorithm and an energy-based optimization technique to automatically track the centerline and the cross sections. The optimization involves the size and eccentricity of the ellipse which best fits the aorta contour on each cross-sectional plane. The method works directly on the original CT and does not require a prior segmentation of the aortic lumen. We present experimental results to show the accuracy of the method and its ability to cope with challenging CT cases where the aortic lumen may have low contrast, different kinds of pathologies, artifacts, and even significant angulations due to severe elongations.ResultsThe algorithm correctly tracked the aorta geometry in 380 of 385 CT cases. The mean of the dice similarity coefficient was 0.951 for aorta cross sections that were randomly selected from the whole database. The mean distance to a manually delineated segmentation of the aortic lumen was 0.9 mm for sixteen selected cases.ConclusionsThe results achieved after the evaluation demonstrate that the proposed algorithm is robust and accurate for the automatic extraction of the aorta geometry for both normal (with and without contrast) and abnormal CT volumes. Purpose The shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for extracting the main features of the aorta that may be used as a diagnostic aid for physicians. This article presents a new fully automated algorithm to extract the aorta geometry for either normal (with and without contrast) or abnormal computed tomography (CT) cases. Methods The algorithm we propose is a fast incremental technique that computes the 3D geometry of the aortic lumen from an initial contour located inside it. Our approach is based on the optimization of the 3D orientation of the cross sections of the aorta. The method uses a robust ellipse estimation algorithm and an energy-based optimization technique to automatically track the centerline and the cross sections. The optimization involves the size and eccentricity of the ellipse which best fits the aorta contour on each cross-sectional plane. The method works directly on the original CT and does not require a prior segmentation of the aortic lumen. We present experimental results to show the accuracy of the method and its ability to cope with challenging CT cases where the aortic lumen may have low contrast, different kinds of pathologies, artifacts, and even significant angulations due to severe elongations. Results The algorithm correctly tracked the aorta geometry in 380 of 385 CT cases. The mean of the dice similarity coefficient was 0.951 for aorta cross sections that were randomly selected from the whole database. The mean distance to a manually delineated segmentation of the aortic lumen was 0.9 mm for sixteen selected cases. Conclusions The results achieved after the evaluation demonstrate that the proposed algorithm is robust and accurate for the automatic extraction of the aorta geometry for both normal (with and without contrast) and abnormal CT volumes. The shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for extracting the main features of the aorta that may be used as a diagnostic aid for physicians. This article presents a new fully automated algorithm to extract the aorta geometry for either normal (with and without contrast) or abnormal computed tomography (CT) cases.PURPOSEThe shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for extracting the main features of the aorta that may be used as a diagnostic aid for physicians. This article presents a new fully automated algorithm to extract the aorta geometry for either normal (with and without contrast) or abnormal computed tomography (CT) cases.The algorithm we propose is a fast incremental technique that computes the 3D geometry of the aortic lumen from an initial contour located inside it. Our approach is based on the optimization of the 3D orientation of the cross sections of the aorta. The method uses a robust ellipse estimation algorithm and an energy-based optimization technique to automatically track the centerline and the cross sections. The optimization involves the size and eccentricity of the ellipse which best fits the aorta contour on each cross-sectional plane. The method works directly on the original CT and does not require a prior segmentation of the aortic lumen. We present experimental results to show the accuracy of the method and its ability to cope with challenging CT cases where the aortic lumen may have low contrast, different kinds of pathologies, artifacts, and even significant angulations due to severe elongations.METHODSThe algorithm we propose is a fast incremental technique that computes the 3D geometry of the aortic lumen from an initial contour located inside it. Our approach is based on the optimization of the 3D orientation of the cross sections of the aorta. The method uses a robust ellipse estimation algorithm and an energy-based optimization technique to automatically track the centerline and the cross sections. The optimization involves the size and eccentricity of the ellipse which best fits the aorta contour on each cross-sectional plane. The method works directly on the original CT and does not require a prior segmentation of the aortic lumen. We present experimental results to show the accuracy of the method and its ability to cope with challenging CT cases where the aortic lumen may have low contrast, different kinds of pathologies, artifacts, and even significant angulations due to severe elongations.The algorithm correctly tracked the aorta geometry in 380 of 385 CT cases. The mean of the dice similarity coefficient was 0.951 for aorta cross sections that were randomly selected from the whole database. The mean distance to a manually delineated segmentation of the aortic lumen was 0.9 mm for sixteen selected cases.RESULTSThe algorithm correctly tracked the aorta geometry in 380 of 385 CT cases. The mean of the dice similarity coefficient was 0.951 for aorta cross sections that were randomly selected from the whole database. The mean distance to a manually delineated segmentation of the aortic lumen was 0.9 mm for sixteen selected cases.The results achieved after the evaluation demonstrate that the proposed algorithm is robust and accurate for the automatic extraction of the aorta geometry for both normal (with and without contrast) and abnormal CT volumes.CONCLUSIONSThe results achieved after the evaluation demonstrate that the proposed algorithm is robust and accurate for the automatic extraction of the aorta geometry for both normal (with and without contrast) and abnormal CT volumes. |
| Author | Tahoces, Pablo G. Alvarez, Luis Santana-Cedrés, Daniel Gomez, Luis Mazorra, Luis González, Esther Carreira, José M. Esclarín, Julio Cuenca, Carmelo Trujillo, Agustín Alemán-Flores, Miguel |
| Author_xml | – sequence: 1 givenname: Pablo G. orcidid: 0000-0001-5245-2349 surname: Tahoces fullname: Tahoces, Pablo G. email: pablo.tahoces@usc.es organization: Department of Electronics and Computer Science, Universidad de Santiago de Compostela – sequence: 2 givenname: Luis orcidid: 0000-0002-6953-9587 surname: Alvarez fullname: Alvarez, Luis organization: CTIM, Universidad de Las Palmas de Gran Canaria – sequence: 3 givenname: Esther surname: González fullname: González, Esther organization: CTIM, Universidad de Las Palmas de Gran Canaria – sequence: 4 givenname: Carmelo surname: Cuenca fullname: Cuenca, Carmelo organization: CTIM, Universidad de Las Palmas de Gran Canaria – sequence: 5 givenname: Agustín surname: Trujillo fullname: Trujillo, Agustín organization: CTIM, Universidad de Las Palmas de Gran Canaria – sequence: 6 givenname: Daniel surname: Santana-Cedrés fullname: Santana-Cedrés, Daniel organization: CTIM, Universidad de Las Palmas de Gran Canaria – sequence: 7 givenname: Julio surname: Esclarín fullname: Esclarín, Julio organization: CTIM, Universidad de Las Palmas de Gran Canaria – sequence: 8 givenname: Luis surname: Gomez fullname: Gomez, Luis organization: CTIM, Universidad de Las Palmas de Gran Canaria – sequence: 9 givenname: Luis surname: Mazorra fullname: Mazorra, Luis organization: CTIM, Universidad de Las Palmas de Gran Canaria – sequence: 10 givenname: Miguel surname: Alemán-Flores fullname: Alemán-Flores, Miguel organization: CTIM, Universidad de Las Palmas de Gran Canaria – sequence: 11 givenname: José M. surname: Carreira fullname: Carreira, José M. organization: Complejo Hospitalario Universitario de Santiago (CHUS) |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30244307$$D View this record in MEDLINE/PubMed |
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The shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for... The shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for extracting... PurposeThe shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for... |
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| SubjectTerms | Algorithms Aorta Aorta - diagnostic imaging Automation Computed tomography Computer Imaging Computer Science Contours Coronary vessels Cross-sections Diagnostic systems Feature extraction Geometry Health Informatics Humans Imaging Imaging, Three-Dimensional - methods Medicine Medicine & Public Health Optimization Optimization techniques Original Article Pattern Recognition and Graphics Pattern Recognition, Automated - methods Physicians Radiology Reproducibility of Results Segmentation Shape Surgery Tomography, X-Ray Computed - methods Tracking Vision |
| Title | Automatic estimation of the aortic lumen geometry by ellipse tracking |
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