Fully-automatic left ventricular segmentation from long-axis cardiac cine MR scans.
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| Title: | Fully-automatic left ventricular segmentation from long-axis cardiac cine MR scans. |
|---|---|
| Authors: | Shahzad, Rahil1 r.shahzad@lumc.nl, Tao, Qian1, Dzyubachyk, Oleh1, Staring, Marius1, Lelieveldt, Boudewijn P.F.1,2, van der Geest, Rob J.1 |
| Source: | Medical Image Analysis. Jul2017, Vol. 39, p44-55. 12p. |
| Subjects: | Left heart ventricle, Image segmentation, Image analysis, Cost effectiveness, Health risk assessment, Magnetic resonance imaging |
| Abstract: | With an increasing number of large-scale population-based cardiac magnetic resonance (CMR) imaging studies being conducted nowadays, there comes the mammoth task of image annotation and image analysis. Such population-based studies would greatly benefit from automated pipelines, with an efficient CMR image analysis workflow. The purpose of this work is to investigate the feasibility of using a fully-automatic pipeline to segment the left ventricular endocardium and epicardium simultaneously on two orthogonal (vertical and horizontal) long-axis cardiac cine MRI scans. The pipeline is based on a multi-atlas-based segmentation approach and a spatio-temporal registration approach. The performance of the method was assessed by: ( i ) comparing the automatic segmentations to those obtained manually at both the end-diastolic and end-systolic phase, ( ii ) comparing the automatically obtained clinical parameters, including end-diastolic volume, end-systolic volume, stroke volume and ejection fraction, with those defined manually and ( iii ) by the accuracy of classifying subjects to the appropriate risk category based on the estimated ejection fraction. Automatic segmentation of the left ventricular endocardium was achieved with a Dice similarity coefficient (DSC) of 0.93 on the end-diastolic phase for both the vertical and horizontal long-axis scan; on the end-systolic phase the DSC was 0.88 and 0.85, respectively. For the epicardium, a DSC of 0.94 and 0.95 was obtained on the end-diastolic vertical and horizontal long-axis scans; on the end-systolic phase the DSC was 0.90 and 0.88, respectively. With respect to the clinical volumetric parameters, Pearson correlation coefficient ( R ) of 0.97 was obtained for the end-diastolic volume, 0.95 for end-systolic volume, 0.87 for stroke volume and 0.84 for ejection fraction. Risk category classification based on ejection fraction showed that 80% of the subjects were assigned to the correct risk category and only one subject (< 1%) was more than one risk category off. We conclude that the proposed automatic pipeline presents a viable and cost-effective alternative for manual annotation. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Image Analysis is the property of Elsevier B.V. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 123374516 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Fully-automatic left ventricular segmentation from long-axis cardiac cine MR scans. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shahzad%2C+Rahil%22">Shahzad, Rahil</searchLink><relatesTo>1</relatesTo><i> r.shahzad@lumc.nl</i><br /><searchLink fieldCode="AR" term="%22Tao%2C+Qian%22">Tao, Qian</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Dzyubachyk%2C+Oleh%22">Dzyubachyk, Oleh</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Staring%2C+Marius%22">Staring, Marius</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lelieveldt%2C+Boudewijn+P%2EF%2E%22">Lelieveldt, Boudewijn P.F.</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22van+der+Geest%2C+Rob+J%2E%22">van der Geest, Rob J.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Image+Analysis%22">Medical Image Analysis</searchLink>. Jul2017, Vol. 39, p44-55. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Left+heart+ventricle%22">Left heart ventricle</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+effectiveness%22">Cost effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Health+risk+assessment%22">Health risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With an increasing number of large-scale population-based cardiac magnetic resonance (CMR) imaging studies being conducted nowadays, there comes the mammoth task of image annotation and image analysis. Such population-based studies would greatly benefit from automated pipelines, with an efficient CMR image analysis workflow. The purpose of this work is to investigate the feasibility of using a fully-automatic pipeline to segment the left ventricular endocardium and epicardium simultaneously on two orthogonal (vertical and horizontal) long-axis cardiac cine MRI scans. The pipeline is based on a multi-atlas-based segmentation approach and a spatio-temporal registration approach. The performance of the method was assessed by: ( i ) comparing the automatic segmentations to those obtained manually at both the end-diastolic and end-systolic phase, ( ii ) comparing the automatically obtained clinical parameters, including end-diastolic volume, end-systolic volume, stroke volume and ejection fraction, with those defined manually and ( iii ) by the accuracy of classifying subjects to the appropriate risk category based on the estimated ejection fraction. Automatic segmentation of the left ventricular endocardium was achieved with a Dice similarity coefficient (DSC) of 0.93 on the end-diastolic phase for both the vertical and horizontal long-axis scan; on the end-systolic phase the DSC was 0.88 and 0.85, respectively. For the epicardium, a DSC of 0.94 and 0.95 was obtained on the end-diastolic vertical and horizontal long-axis scans; on the end-systolic phase the DSC was 0.90 and 0.88, respectively. With respect to the clinical volumetric parameters, Pearson correlation coefficient ( R ) of 0.97 was obtained for the end-diastolic volume, 0.95 for end-systolic volume, 0.87 for stroke volume and 0.84 for ejection fraction. Risk category classification based on ejection fraction showed that 80% of the subjects were assigned to the correct risk category and only one subject (< 1%) was more than one risk category off. We conclude that the proposed automatic pipeline presents a viable and cost-effective alternative for manual annotation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Image Analysis is the property of Elsevier B.V. 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: 10.1016/j.media.2017.04.004 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 44 Subjects: – SubjectFull: Left heart ventricle Type: general – SubjectFull: Image segmentation Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Cost effectiveness Type: general – SubjectFull: Health risk assessment Type: general – SubjectFull: Magnetic resonance imaging Type: general Titles: – TitleFull: Fully-automatic left ventricular segmentation from long-axis cardiac cine MR scans. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shahzad, Rahil – PersonEntity: Name: NameFull: Tao, Qian – PersonEntity: Name: NameFull: Dzyubachyk, Oleh – PersonEntity: Name: NameFull: Staring, Marius – PersonEntity: Name: NameFull: Lelieveldt, Boudewijn P.F. – PersonEntity: Name: NameFull: van der Geest, Rob J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 13618415 Numbering: – Type: volume Value: 39 Titles: – TitleFull: Medical Image Analysis Type: main |
| ResultId | 1 |
