Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT.
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| Title: | Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT. |
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| Authors: | Cha, Jungwon1 j0cha001@louisville.edu, Farhangi, Mohammad Mehdi1, Dunlap, Neal2, Amini, Amir A.1 |
| Source: | Medical Physics. Jan2018, Vol. 45 Issue 1, p297-306. 10p. |
| Subjects: | Pulmonary nodules, Image segmentation, Lung cancer, Intensity modulated radiotherapy, Cone beam computed tomography |
| Abstract: | Purpose: We have developed a robust tool for performing volumetric and temporal analysis of nodules from respiratory gated four‐dimensional (4D) CT. The method could prove useful in IMRT of lung cancer. Methods: We modified the conventional graph‐cuts method by adding an adaptive shape prior as well as motion information within a signed distance function representation to permit more accurate and automated segmentation and tracking of lung nodules in 4D CT data. Active shape models (ASM) with signed distance function were used to capture the shape prior information, preventing unwanted surrounding tissues from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend three‐dimensional (3D) segmentation to 4D by warping a prior shape model through time. The algorithm has been applied to segmentation of well‐circumscribed, vascularized, and juxtapleural lung nodules from respiratory gated CT data. Results: In all cases, 4D segmentation and tracking for five phases of high‐resolution CT data took approximately 10 min on a PC workstation with AMD Phenom II and 32 GB of memory. The method was trained based on 500 breath‐held 3D CT data from the LIDC data base and was tested on 17 4D lung nodule CT datasets consisting of 85 volumetric frames. The validation tests resulted in an average Dice Similarity Coefficient ( |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 127273282 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cha%2C+Jungwon%22">Cha, Jungwon</searchLink><relatesTo>1</relatesTo><i> j0cha001@louisville.edu</i><br /><searchLink fieldCode="AR" term="%22Farhangi%2C+Mohammad+Mehdi%22">Farhangi, Mohammad Mehdi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Dunlap%2C+Neal%22">Dunlap, Neal</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Amini%2C+Amir+A%2E%22">Amini, Amir A.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Jan2018, Vol. 45 Issue 1, p297-306. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Pulmonary+nodules%22">Pulmonary nodules</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Lung+cancer%22">Lung cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Intensity+modulated+radiotherapy%22">Intensity modulated radiotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Cone+beam+computed+tomography%22">Cone beam computed tomography</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: We have developed a robust tool for performing volumetric and temporal analysis of nodules from respiratory gated four‐dimensional (4D) CT. The method could prove useful in IMRT of lung cancer. Methods: We modified the conventional graph‐cuts method by adding an adaptive shape prior as well as motion information within a signed distance function representation to permit more accurate and automated segmentation and tracking of lung nodules in 4D CT data. Active shape models (ASM) with signed distance function were used to capture the shape prior information, preventing unwanted surrounding tissues from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend three‐dimensional (3D) segmentation to 4D by warping a prior shape model through time. The algorithm has been applied to segmentation of well‐circumscribed, vascularized, and juxtapleural lung nodules from respiratory gated CT data. Results: In all cases, 4D segmentation and tracking for five phases of high‐resolution CT data took approximately 10 min on a PC workstation with AMD Phenom II and 32 GB of memory. The method was trained based on 500 breath‐held 3D CT data from the LIDC data base and was tested on 17 4D lung nodule CT datasets consisting of 85 volumetric frames. The validation tests resulted in an average Dice Similarity Coefficient (<italic>DSC</italic>) = 0.68 for all test data. An important by‐product of the method is quantitative volume measurement from 4D CT from end‐inspiration to end‐expiration which will also have important diagnostic value. Conclusion: The algorithm performs robust segmentation of lung nodules from 4D CT data. Signed distance ASM provides the shape prior information which based on the iterative graph‐cuts framework is adaptively refined to best fit the input data, preventing unwanted surrounding tissue from merging with the segmented object. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mp.12690 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 297 Subjects: – SubjectFull: Pulmonary nodules Type: general – SubjectFull: Image segmentation Type: general – SubjectFull: Lung cancer Type: general – SubjectFull: Intensity modulated radiotherapy Type: general – SubjectFull: Cone beam computed tomography Type: general Titles: – TitleFull: Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cha, Jungwon – PersonEntity: Name: NameFull: Farhangi, Mohammad Mehdi – PersonEntity: Name: NameFull: Dunlap, Neal – PersonEntity: Name: NameFull: Amini, Amir A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 45 – Type: issue Value: 1 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
